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<?xml version="1.0" standalone="yes"?> <Paper uid="J79-1034"> <Title>S-P S : A FORMALISM FOR SEMANTIC INTERPRETATION AND ITS USE</Title> <Section position="2" start_page="0" end_page="0" type="metho"> <SectionTitle> !375 ACL Meeting SYNTACTIC PROCESSING IN THE BBN SPEECH UNDERSTANDING SYSTEM MADELINE BATES </SectionTitle> <Paragraph position="0"> Bolt Belranek and Newman Inc.</Paragraph> <Paragraph position="1"> The syntactic analysis system presented here is composed of ~vo parts, a modified augmented rransition network grammar and a 7arser which is designed for a speech understanding environment. The parser operates on partial utterances called th&ries.</Paragraph> <Paragraph position="2"> A t3eory may be thought of as a set of words which are hypothe? sized to be in the uttezance. The parser processes the words in a theory by building partial syntactic paths using the words of :he theory. These paths do not depend on left context, which will Le missing if there are gaps in the theory. Syntact-ic constituexs are built where possible and, whenever a constituent is it the parser can interface witrh the semantic zomponent of he total. s~eech understanditzg system for guidance and verification. null The parser tries to predict words and/or syntactic categories 50 fill or reduce gaps in the theory, particulatly small function riorls which are difficult to detect reliably on acoustic grounds aloone. Thk parser does not follow all possible parse paths, but nctc~;:~pts to select the.nost likely ones fo; extension. It uses a '-:dicious mixture of top down, bottom up, depth first, and breadth</Paragraph> </Section> <Section position="3" start_page="0" end_page="0" type="metho"> <SectionTitle> J &quot; I </SectionTitle> <Paragraph position="0"> rlrst parsing strategies to take advantage of local, reliable inforrLstion. It saves all the information gained while following al~crnative parse paths, so that several parse paths which share a comnon part, even if the paths are in different theories, can stare chat portion without ~eparsing. This is true even if the -.rse r/ '.A paths split before and/or after the common part and even if the common section analyzes only part of a syntactic constituent.</Paragraph> <Paragraph position="1"> kn~wlcdg@r each Playing a particular role -elurlmsg the interprctatlen sf an utterance, #hi le thede rolcrj are intcrrclatad, it Is lmportant to oc agle to separate the kn0wIedge sources so that InterPelatlans arc vlslbbe and so that the cantrlbueisns from the variaus sQurecs can be studiedo The knowledge sources uged in tht system belnq devclo~ed jointly by SPJ and SBC can bc characterized broadly under tns headings of 860~6tic~~ syntaxI semantlcsp and QiscQusse (wslkcr ?t alQp 19951 Pablnban, 18753 Hendrlx, 19751 Deutgch, 19758 loc cum, 19751 Pitea, 19751, Thc acoustic component relates 1f-nqu1~tbc @nFithe~ ('#~rd$ and phrases) to the s~gcch wavefst~, An acsus~%c-~hon@t$c processor enalyzcs tnc digitized waveform t~ extract paramctcrj based on Spcceh ~r~dU@tl~n characteristicsa The Darametefs ihcludc fundamentab Lgequency~ volein3 Labelo fQrfl&nt frequencyp energy data, and athers, FollowinQ parameterlzatlan, varis~us rules @a@ ~ppbtard to gepa~ratc BPI ac0~3tf.c feature d~5e~:l~ti~n of the uttcranct, The parameter6 and ksatures are ~ubs@quen?ly used by the Laxlcel m~plpiglca proccdiaza, he mapp@r 1s called fILarhci the parsing of an utterance to q$ve a. deed~icsn score as to whether a progored word or phrase could actl~ally he prqaent in t~ sPaci f Icd time region ka l %PAC Input, Phonoloqlcal ~nd &~a~stle=phbn~tlt rules are used by the mapper to r~lqt~ nhonsttc spelling$ ta acauetic AaEa, Syntu provides rclirblc~ reasonably incxpenliv~ lndlcatlanr of ikleh words or groups of words may ca~bine and of ho* xcil thty fit, Syntactic ruler plve general psttrrns for conrtructlng noun phraser, claustrr and scntencgs a?d ~ro~ide censlstency eRccks tor such iterr as nurrber agreement, In testlr:q word or Phrarc c~mbinati6nSt syntactic Infarmatior? alone can often rule Out a candidate without the I7ecd tor nore costly serantic ana dttcourac analySlfir Tht semrntlc component includes a general p~del of- the domain of dlgeaurser and e set of a2gorithvg for ca~biniqg (ox tcjcctlngl concepts in the domain, For txrmplct given a verb apd two noun phraser, semantic routines can build the corresponding @ctMintie ralation between the Items indicated by the noun phrases, The df~course component deals with the pelattanship of the current utterance (or a portion of Lt) ta the dialog context and ta entfties In the task dsmain, DLscourse functions U6e infarmatlon fron prtvious uttcrancaa to fill out elliptical sxpresslons and to find referent8 far Pronouns and derrnite noun</Paragraph> </Section> <Section position="4" start_page="0" end_page="0" type="metho"> <SectionTitle> P~TL~~CI I </SectionTitle> <Paragraph position="0"> The Irhguage detlnltion is the foe41 paint for Integrating the#@ kn~wl~d~a sources, A lanquage definition Includes (1) set8 of Units out of which Uttafanesr in the languaue arc csnotrbctcd and (2) rult8 for combining the units into lar~cr structure@, The bcrit unit8 will be cdll@d 'wordsc (although th16 technicel use deer not exactly correspond to the common use), The camPorttion rub66 indicate how Phraseg Can be combined into otill l~rgar phta~c~~ Mere ~~@c~scZY~ a, phrarcp Ir 4lther a word in the input ox thc result of 8pplylnP a composition rule to constituent phPe@cBc Tne rules give the Lldtar Pattern ot constituents and .rpeclfieations feu calculating values for bath the attrtbutel of the tcrulflnp Phrase and i~r factors used in judging the rasult, ft its at the phraa~ lave1 that the knowledge sources are tntrqrattd into the 8ystrm, Thcrc are two aspects to the ~OntflbutlDns trom eren sourcel the values df properties of the phrah ar computed by the knowledge aourqe, and tne saurcec8 arr@rrmsnt of the cart6ct:naser or th1,lo phrase a8 an interpretation of the inpot, There two aopectr are reflected in the attribute and factor statement8 that ,are assotlattd with Bach of the words and Phrase, in the lrhguagr d@tini&lonr Thr atrfibutc statements provide lnatryetlonr for eomputlnq var lour prapcrticc of the Phra*a, There inrtructiahr may call Won any or all of the oaurccs of knouledua, ~sr axampla for a phfdse spanning a particular ragmarit, an aenustic &ttrlbuta may specify the ward8 in that sagrnantl an attribute Supplied by the syntax ean sPacleY r farture rueh rr thr valce (*retlvrP or Qgaaol~cC)~ an attributa Lbpplled by remrnticr errn 'Ipecify r semantic net kdterpretatton built trom the rernantfcr of the con@tituantap and an attribute rupplied by the die~ourae componant can indicate a tofercnt or an impLLed meaning, P8cfor strtltlntntr tall how to u.r@ these attrlbutrr in dWm!tlnihg the likelihood that the Phrarc it corrret $ntcrpretatlon ot thr input* The result of eosblnlnq the factors tor u prrtlcul&r phrilt 1 eJlcd a reorc, Thr urc of such gcortr by the executive in determlntng averall itrateqy fl described bt10~1 Fact098 are nonbinaryf tinca they can have a range of valuer, rLgid *yere or 'no* dt~ist~nb 64 not have tc be lad@ in esrersgng the qUalltY of a Pht.#8ee For axa@Ple, the cloransre of the acourtlc match asy vary @nu this CB~ be rrtlecttd 1 the corrctPondlng factor, weak evidence from one rource of knowZtdgc could lower the rcora, r hi la strong evidence from amther 80urch cwld c6mpen8ata for that and actually raise the SCO~-4, In summatyr a phtare is 4 eompo~lte intarpratation of a ~articu1.r portion of tne utterrnei, inttgr~tinp contributLans from a11 ialavant knawledg@ oaur~t~ r This mean8 that each pertian ot the lnput is lntt'rpreted and evaluated by the system ar fully a@ P60Sfb&~r as 800n 41 po~~lble , Tne 8y8tw is navcr faced with the problem of rtXatlnp ar eomblninp freqmvntary thearier conlttucted Inde~tndantl~ bY dif fr~rent knowlrdus 8eurcl)s~ and cvrluarlanr mrdr by differant sources are Lnirdlrtely mrrqcd to e~ntrol and CoOrdlnrte ovrr@ll ryatcm activity, For QXI~PICI a8 goon &f r dafinlre noun Phr&I@ i8 found# the acourfic component chcckr thr corrttculatlan of the eanrtltuentr, the ryntretic component check@ for agr~ement in trrturer 6~eh ar number, the rcmmtic component build8 II r~prasrntrtlon of the meaning, and the direourre component looks far r referent, The fallmin~ tx@apke lllutttatQp haw Several knowledg@ ~b~tces ate used together to htet~tet and evaluate phraree, The Submarine.* or 'their submarinar@ and t1l;urtrates t integration QS acauotret ryntactle~ remantic@ and dlrcourre Lnformarfon,</Paragraph> <Paragraph position="2"/> </Section> <Section position="5" start_page="0" end_page="0" type="metho"> <SectionTitle> DIICALL(~~DIBRWP~,$&MAN~:ICS) ELBY ~UUND~EPXNEV, IMTtRPRETATXON t IF DI8C0URSE bU HUNDEFINtD THEN DIlCOURIE ELSE SEMIHTXCBt COIR'T a MAPPER(L~~TWOFD(DET),VIR~TWORB~~QM)), </SectionTitle> <Paragraph position="0"/> <Paragraph position="2"> rr8ultrnt phrralb, which In an acoustic attri.bute indicating the ward8 campori~g thir phrwe, NBR (nllmber) and CMU (ca~ntmmr88-Unit) axe UynZ&ctlc rEttibUtgS tar the Phrraa. each being d~rlvsd from the Intar8rctlon af the eorra$pondinp rttrlbutea of the con&tltuanrrr he ravrnticr attribute is a piece of semantic net that is constructed from tnt remantles of the canrtttucntr by the stmantle routine (SkMRNP7) araociated wlth this rula, If the MOOD att-ributa of the DET can~tituent is 'DEEC, 1 declarative determiner, then the dlscautse rautincs wilL leak for a referent for the Phrase in the dialog context and 8srlQh its ramdntlc structure a$ the value of the attribute PXSCOURSE, The INTERPRETATION of the phrase 16 efther the referent found by dl~c~urrt or the semantic net structure In case no direct reference is found@ The frctoi statements use these attributes in carnp~tfns cantrtbutianr tawardr the seara for the Phrase, As ha$ been aentloned~ there fc 4 rmpe of aecaptaBlc valuer for factorr, Par rimpllcity, Symbolie valuer are used (VEFYCOOP, GOOD, OL, POOR# BADt and OUT], Zn the cxamPlt ruler there are factors deterrnlned by saeh af the major knowledge sources, The CaART factor reflects rp acoustic :crt of the coartlculation of the kart w~zd of the clet~~mintf and the first word of the nominail NeR and CMU arc ryntictlc factor8 that will eliminate the phrase it either attribut4 1% ineomprtlblc bttvccn the conrtitucnts.</Paragraph> <Paragraph position="3"> The ranantic factor *ill rllminate the PhFrgc if no remantie Intrrpt+tstlan can be tarmulatad, While the current remantic camgonsnt bat6 not hrva a metric for d@thrmining the likelihood of @n intcrpretdbtlon other th.n whether or not a Semantic rsprertntation can be butit, it ir pos~ible to intmducc auch a nrwic and hrvt the irmrnt%c factor# be nonbinrry, The discourse trctor i& nonbinary, ~f the determiner 1s declarative, the dlseourse h&8 tried to find a referent, If no referent War faund, the factor is given r low value, *POORpr but the phrase is not diacsrded, If scvcrrl porrlbls referents were found, the phF88e 16 kept and the score is not lowered because the ambiguity can perhags be resolved later, It just one referent war found, It is taken ao evidence that the phraoa 1s a correct interpretati-an for t,hat Porttan of the Utterance and the factor&quot; is given a higher value 'GOOR@, The example discusead i#bOv@ shows how the langua~,~ definition sygtom can be used to integrate a variety of knowledge rourccr in a Wry that keeps the cantributlonr and interactions of the ditf went sautcrr earily vis&bltr Tns teprercntatlon com~lncr procedural Infatmetion (in the exprtsliana for c~lculating attxlbutt and factor Values1 end declarative inforrnrtlan (in the constituent pattern) in a form designed to rimplify thc task of writing a large! definition containing many rules, Howrvrr, brfore fhc ruicc can actually ae Usrd, thr~ muat bcs convert cd to a ditfcrcnt rapraoenration d~eigned with afficdency In mind, This tWhn8lati0n is dona by s language definition compile'l:' that cemtructr an internal rcprcrentation of the language definition that depend% in an intricate way on the rtructura of the &quot;a~scut~~c~~ the portion of the $YStem g!@#pOnlibLI for rchedultnp and eontrolling the various tarks to be Perfarmad in conrtructlnp an Inter~ratation of an utterance, The operation of the exrcutiva is the subject of the rest of thir The axhcutivt maker a dirtlnc'tlon between tho phrases bclnp built and the tarlcr rkquircd to build these phrases, A data StrUetUrer called the 'parre net', tdpreeants the growing collcctlon of phrase#, snd another structure, called the 'task qUeut@, Cnc0de8 the rltcrnrtlve operrti*nt available for taklng another Step toward Understanding the input, Each enttv in the tarK queue specltier r proccdurt to be performed at a particular locatton (node) In the parse net, The ~e%torrnancc ot such a proctd~~C typically entails both modifying the parse net and Seh@dUlLng naw task8 to makc further rnodltlcattons, Each task has rssocl~ted with it r priority for pctf&quot;ormlng It. The method for determining priaritier is described below, Taskr can include lpaklng for a new word or phrase to finish an 1ncomBlett Phraoa [one mtssfng some of itr constitutnts] and frying to ure r word or completed phrase in a larger phrase, This men8 that the ryrtem can work both 'tap downr and 'bottom upel btcaUlt it can look in a goal-driven manner for mlrring eonstltutnts of hlgher level phraiest and it alto can accept word8 from the aceurtlcr to bulld into lrrgar phrbrcr in r data-driven mannerr AS an exampkc, COnsidtt the simple grammar</Paragraph> <Paragraph position="5"> Assume that,the word @theyo has been7 found initLally either by the acoustics directly or as a result of confirming a Dredietlan made by The language ?ertnitLon, .They4 constitutes a compltte NP, This NP cah be gut into the S rule, cau$ing the partially filled phsase 'they VP* to be added to the parse net, Alread~~ Some of the attributes and factors tor the S rule can be drtermlned, and r score computed for this phrase, Bullding this partial phrase loads to the creation of a ntw task4 to look for a VP falloWLng the hP, That task in turn leads to two alternative 8ubtagK~I Laak for a VP NP or look far a VERB, PriarLtLrs far both these tasks are computed and they are put on the ta$k queue to be ProC@$Sadr Tho executive then removes the next task frov the queue and continues, In Penepalc decldin$ which task to perform is of great Importance, bccaure only a subset ot the scncduLed taws will actUallY Prove ro be neeassarY to Understand the ingut; the ethers will be *false steps* leading to dead anqs, ~deailyr in dcclding wh-ich task to do1 the executive would always Choose one of the ntecsoary fa8ks and never take a false step, The utterance would ba undarrtoad with the unnecessary tasks still left $h the Queue, Ta aP~rgech this idealr thc actual system must spend rome nf its effort In chodaing tasks, Such offorg is well spent if it producca a net decrease in processing time, In other Wordrr the etticitncY of the system will b~ 1rnPcoVed by deci$ionn regarding the order in which tasks are perfotmed, if the cost a!! the decirlons la loss than the coat of the false rttpr that wauld otherwise hsvc been taken, Since ac+ouatic uncertainty in $patch undersfanding makes tnt potontlai for rartlng effort en unnecessary operattons particularly large, the system can afford to carry ,out rather complex computations In deciding what to do next and Still obtain a large imrovcment in overa$l efffcltncy. In the current system, the decisions are based on the rclativt priorities assigned to the varlous tasks Waiting In the queue, Taska ere aSS*oclated with phtaser, and task pzlorltier lrrqtly depend an how important the system feels it is to Process the Phrase, In addition to the scores of Phrases, which combine a variety of factors but awe LndePendant of the larger gentential COntUtr the system form8 another arstrsmcnt of the quality of the phrase called the phrase 'valuee, which depends on the context of proPoScd complete lntcr~retrtionr for the entire utteratwe, The phra~t value is an estimate of the highcat score EUor ell posslbla intetprrtationr spanning the utterance that Include the PhtaSt, The tltlnatt 18 tzornputcd by means at a heuristic atarch of the space at por.8 lble rentcntial contexts tltablilhed during thc PreViQUS tarks Pertarmed by the cxeeutlvc, The ~~16fif~ of a tagk Is inttially rut to the value of its aSS@cia0cd Phrrstt but thr Priorlty 18 lowcrcd it the tsar conflicts with the cxccutfvc@s cuortnt 'focur of aetivity&quot;, The phrarr Vlfu@ that dttcrmln.8 the initial prLorlty reflect8 an evaluation of both the internal rtrdcturc of the Phrase and its te)rtlon to its context, but it does not reflect its competition, If a Phrase he8 e high OalUe, other Similar Phrases are also likely to have high valbes, If values alone determined prlorltles, then even after successfully completing a Phrase, the system would tend to cantlnut looking for minor variations In the lame area rather thae moving on to Look far ways to construct a cgmplate interpretatton, The afacup df activityc mechanism provldts a way for phrases to inhibit the executive from looking for cbmpt@lnp phralts that would nece~sarily fcplacc them. This focusing 11 btoughg about by lawexfnq tha priority of tasks that look far r~~laecmcnts for any of e Set at focus ~hrases~/until the potential replacement pramlses to lcad to e aigniflcant irnpravcmcnt in Value for the final interpretationd The effect is to bias the sxecut-ivc toward bullding UP a complete inter~xetatlon using phrases in focus rather than exploring competing interprttatians that wauld not use focus phrases, X f the focus 8 wrong, the attempts to extend it to a complete Interpretation will be unrucceS4fUl. Eventually a taok that eanflicts with tne rocus wiir Decame the highost pr'idiity c>Perrtlan far the e,xtcutl,vrc to Pstfarq in aplte of the bias 49rin8t Itr At a result the focus Will be modlfl~d so that it Is consistent with t,he new task, and tha, executive wllL then, cancentrate an urlna the raVl$ed ssr az Phrases, In addition to ealeulrtlnq priotltLes of tasks on the basta of Phrrrt value6 and focus of activity4 the executive must cnrurc that tha informattan gained through tho performanca of the task8 s used etf~ctlvely, This ir done by strueturinq the parse net and the tasks that operate on it in a wry that brings together related actfvltier and caordinstes thrm to tllmlpctc duplication of effort, By avoiding dupLLcrtion, the rytttm rcduleeb the fU effects of the false step8 It will inevitabty taKe, Work dane on a false path fr not necerserily wartedr slncr it may produce a Phrase that can be ustd in soma other Way, for exampler a RPiraSe eonrftucttd ir part of an unsuccerrful search fog ant! type of Sentence may later appear in fhc tlnal interpretation sS Part of a dlfiarent kind of stntsncce AlSOr false steps arc not repeated, since the system only makes one attempt to build a partteular type of Phfrse Ln a P%rtl~Ul~t Zoca,tion in the utterancar rtgardl@ss at how many larger phrases might include it, Mlatakta are intvttabls, but at least the oytttm will not make tbf &&me rn1~rrre twgce in ant par$-Ta SUmwarl~t!, the language definition Is designed to facilftrre the IntWrrii~n of many knowledge sa~tc@$r Pules in the language dtflnftion cdntaln atttibutes and factors from all of there ~o~tces, The attributes are Urtd ta indicate partitutar proptttitl of phrasest and factors then Urt thc~c attributes to detrrmtnr th. #core of the phrase, The external reprelentation of the language, derlgncd tor easy U8t by people, in converted by a languapc definition compiler into an internal reprarentrtlon, derl~ntd tor efficient use by th@ rxecutlvr, In a step by step manner, thr emeutLvc user this information to create, rvrlurte, and coablne phrrrer, The of the next operation to carry out taker the form of asrlgning prfaritios to alternative tasks, Prlaritie~ ratltct bath the expected values of campltte intarptetation8 toward Which the task would lard and the relation of the task to the current focus of aetlvlty, Flnallyr the entire process is organized so that informatian gainad In Performing e t~6K 16 Shared and recordtd in ruch a way that It does not have to be tedfscovertd,</Paragraph> </Section> <Section position="6" start_page="0" end_page="22" type="metho"> <SectionTitle> ABSTRACT </SectionTitle> <Paragraph position="0"> Thlr paper de1crfb~l a tunaable performance grrtamar) currently being developed tor SpWGh un4~tstrndlng, It rhows how attrtbuter of words ate drfinad and propaqdtcd to ruccrrtivrly larger phraser, how other rttrlbutrr are required, how 'factorr' reference them to 1 the Parser ehoorc araanp cowctin? d.et1nttionr in o.rdet te interpret the utterance correctly, and haw there facterg can easily be changed to* adapt the grammar to othar blseaurrrs and cOnt*xt#r Factors that @iiQht br clu8iftld rs a8Yntcctlc* are amPhrriZedr but the rttributrl they rrteresace nrad not be, end 8eldam are, pu~rly ryntrctic, Thlr ramrtch was rupportrd by the Dtfense Advanced R8@8&rCh Project8 Agrncy of th~ Dlprrtmanr at D~tenae and mongtoxad by tha U,s, Army R*r@rrch Offlca under Contract Ha, DkWC04-75*C-0006, 8 perfotairnce grramrr (PC) d*tihrr tha fatm and meaning at the kind8 of uttQrancer that occur in rpaatanooua dialog, When tnr detinltianr of the grammar RtaVid~ tnformrtfon that htl~s a prtrcr ehaora these rule8 ao8z likely to laad td correct int@tpretatlonr of uttrrtanCs&, the grrar@ar 18 raid to be 9tunrd*, Uhan the tuning 1.8 crarily chrncraa wnrn thr dornrin ot dircaurrr, ehanges, thc grasmrr 16 said to br *tunrrblr#. The ability to tune r grammar &a particularly inportant in spreeh undrrrtanding where the inhatsnt Uncmrtrinty of the input caurer falre path8 through the grammar tb be multipll@d, Thil pa)rr dercribrr a funrablr PG baing developed Jointly by 8RI and SDC for r camputrrrnbrred 8P@@ch underrtanding eystam, 7[t# vocrbulrty and phrr,ro typesr ralscltod frsm pt'otoc44rr rra appropriate fat rrelng and anruering qus8tionr about propettirr ot 8ubarriner. Thr PG now dafiner War 70 word and phraae crtrgorter, Its reape rxtandr far bayand syntax, A dl#~ours@ coaponrnt enrbl~o if to nand&@ rnaphora and ~llipiit~ 4s int *What ir the surtrce dirpL~crsrnt of the Lafeyattr?.l.. What 11 It8 arrf t?&quot;, and &quot;What in the irnuth ot tnr baEayrtte?...,, The Ethan i~len?~ X remantic8 component definer a common meaning far paraphrrsar, b8 gn Vhr Sperd at thr Lafayettr ir $0 knotrH and *the Laf&y)tpe h.8 a rperd of thirty knatrW. (Sar Walker rt al., f97St Paxtan rnd R~bin~ont 1975; nrndriw, lW5t Dautlchr 197dr1 Each dr@lnLtion comporlng thr PC ha8 threr prxtr, The flrst name$ r word crtegoty or a phtr8e Catrgory and pravld@r r cont'rxt-ftrr production far ftr caaparltianr Tha racond part# ca1Ied @rttributcs@, tells how to drtrralne fha propertlea of an Lnrtrncr of thr cateqory. Any detinltibn ern r4tferrn~8 nUltlp2r rourcer of kn~wled~e~~aeourtic~ syntrcttc, rrmanticl dlrcour#e, et prrgmatle-mior infotartton nreded to dutrrminr rttribute ~11~8s I The third part, @fcctorrO, define8 #corer for combtnrtLonr of rtttlbutrr, indicrtlng how wall they Qtlt', 1 t ir through factor rcorer th&f the granscr is tuned, Thr indtvidurl scorer re rmbtned into r eolporltr rcorr which I8 used by the parsar te eh008c rnong competing plr#fnglr A putpattrl lnrtrncr of the deflnitlan rlth r reate of OUT for any factor Ls Lamedlrtely rli@lnrtrdr a Lor score may riinlnrtr r p&tslng path! r high reorr rnhrnerr the priority of I prtrlna path that appller the drLlnLtien'@ Our ~nemanie term8 for factor rcorer 1re VERYCOODt GOOD, OK, POOb BAD, md OUT, There rre trtlartet af likeiihood, They arc neclssarily v8uu1t beCaU8e wt arr drrlinq with grrdurl phrnamrnr and Probabillstfc ttndenct@ar They nrcn tomething like npultt Ilk*LyN r ui~pd~t@dRt rardinary@r &quot;odd but ~c?~~ibl~~~ *unt/kely-lJ8teh rgrinRf rrd &quot;8o SPrelrl that wr do not drfinr Itw, Rlpid, prercriptive judqnontr rrr avaided, Combining wfaatm rkth R-8F a8 r plurrl noun 18 Ind~ed w~dng and therefore OUT, On the other handr @fueln doer Cb@bln@ with plural @-an rlth thr r~relrlitrd ntaning Rklnd@ at furlu. At prerrnt Vlu@Jc\ like Vfsatra 1s judged ta br OUT tot out lrngur~e, but $hi8 judfiteent can ra.r$ly be C%t@tQdr &f we find thrt our lrhgtrrgr uarrr refer to kind8 of fuel rr kt~rlru, liner frgtor scorrr aan br changed without tttecting thr rent of the d@tlniZibnr thr grammar tr tun@rbl@ to diffarrnt direoutre domrlnr and rtylrr of SPrrking. hirot 1f one factor drtlnas r low rcorr for an lnrtrntirtlbn, other$ ary stlL1 tats.</Paragraph> <Paragraph position="1"> the cenporlte score, A rtatlrticrlly improbablr phrarr thrt ~rkrr renrr and !I uttrtrd intrlliglbly should not be unduly dJtilcult to racogn2zr and lnterprrt, The rrrt of thir paper @xrrLnrr tepurncer ot drtinitiont rrquired for prrring and Undrrrtandlnp a typical utterance. We begin wtth word aefinitionr~ and rhow how the attributri sf wardr rfr ~ra~~grted to ruec@rfiivrly larger phrraer, haw athrr rttribut@S prcufiar to high@r*l*V*l phrrrrr at@ addrat an6 how frctorr rsfrrenc* thaw in tuning the grrmmrrr Preceding d$ficourfi@ and unberl~lng asmrntic dlrtinctiona con# train the rurfrce ryntrx of an utterance, Bscaure ruprrfici4l ryntrctic prapcttirr signal thorc constraints, it 11 often econoaicrl to usa ryntretic trcforr in order to alrcontirm r wrona parsing path or confirm r corrdct an*, avoiding @all# on rrtmrnticr P ~LIcou~~@~ and r~ouotlcr bat axpanrlva 1x1-dapth avaluatianr, For r#im~1e, if 8omranr ray8 Vucl rupplirrn, wa do not want th~ prrrrr to rnplorc, In depth the application of rul,rr that build r plural noun-phrlsr tram *furl $,,, II wkthaut: conlldrring an altqrnrtlvr deLlnltLon in which wturl~8 a aoditi*i 0-f r auntablo nomin.1 beginning with So thir end, ua incLudr a frctot that chackr the countablrnarr ot qturlw by rrfrrraclng a countlmarr/unlt (CHU) attribute, which Lu syntax ortented but rrrrntirlly rra&ntlcr barad, txrmple$ of soma uraful ayntrx~orirntrd rttrlbutrr drflnrd for the word category W (noun Btel) rpPIar in (1) belor, Evrry H ha6 r value tor the CNU rtttfbut. drawn from th@ &at (COUNT HAS8 UNIT), NI With the CNU value UNIT (auch I8 &quot;t~~t% 't~fi', Mknotw) eoabinc rrsily WLth pIurrl auiflxer and number cx~tt86~ort~ c~~Q~~ @two kknotaUr nffl~~ fertRlr but not go well with ast initr aetsrrfnrcr these twe knotam), or ~rnfti~~ ruftlrer tathr twenty knot#@ 8Perdal. (Ct, &quot;he Ethan Allrner plural tuffixer and nuabcr exprarrl~n8, and when thry do, the rrantng 18 rpcretrllsrd, To @om@ degrr~., they era like maw Na; @tht+e splrdsVthraa Fetes at 8$@@d) I8 analogour to Vhrre IS itoePtrblr~ while R& fudl of two tgnrfl 18 iL1 farmad, The Cttributr PLIUPF dirtinguirhrr irrrguiar plural8 like *t00fq, Unllkr thr CMv aria RELN rttrlbutrr~ it is purely Atttibutal 4tfectlng the rbildtg to combine with the plural ~~tfi~ br3u @re retatrnced fn the two cornpa8itian rules of (21, defining the crtagoty NOUN, The attribute Statamenfr Fropagate the rttributer at the item, adding a numbrr atfributa (WIR). Tho flrrt factor or N-1 reirrencaa the CMU 4ttribUte and rtrt~la that if the value ir &A$$, then the acora 18 GOOD, This juagmsnt lncorporrter our knoulrdpr that the ether ruler N2, cannot 4pply to mast noon-Stam%, It tha token i8 r mar8 noun-atam, N1 is the rlght campotltion rule Za apply, PLSUFF 8 IF' Pb8UFF EO RNO THEN OUT ELSE OK, cnu IF cnu ta fl(nAa8) Tnrn OUT ~b8t OK, UMIT 1 XF @UNIT T!4 CHU THEN QDOD CLUE OK, REtH 8 LF RE&# EP V THEN POOR ELIB OK! EXAMPrtEU FOOT *$, FUEL -8 (OPT), TONS (GOOD) 8URFACE DISPLACEMENTS (POOR), SUBMARXNES (OK)! bike the CMU frctarr, thr Ft8UFF factarr rnhrnca the #ear@ tor applying N1 to stems that do not take a piur.2 rutfix and Cohltrain NZ not to apply. A RELN factor Onhrncrr thr score whrn s~ba&rlnefl, @thole fualrvnnd accept What furln 8.r OK, while awhich tonsa &end &quot;that draft at f'ive featr ara POOR, Factor8 for NPl1 e;iainrta fU*lHr &quot;1 draft ot tha LafiyetteU~ and la $ubmarlne.r@9 4Cccbpt &quot;4 subrnatinr@# tanw? *the aubmrfneM, and &quot;he 8Ubb.!WS@d spmdU, and Brcote @the tonm and *the dtrtt at tiva Zn arch drttnltion, r UNIT trctot tetrranees the CMU rttrlbuta ot the HP, If the vrlue L8 NIL, the drflnltion 18 not appZIcable. Zf UNIT is I valuI~ than the UIZT factor Yar NP4 score8 the applicrft-on as VERYGOOD. Them arr two rraronr for this judgment. Numb@? axprrrrlons are tYpicrlly found with unit rwpresslonr to fora me&rure rxpterslons, and unit8 are more likely to occur with indetlnitr than wfth dtftnlte focus, rr the ptecedlng example8 Catventy Knots* and so on) have Lndicated.</Paragraph> <Paragraph position="2"> Since the focur for NP7 i8 dlwrys drflnttet the UNIT frctor 8ecrerses the wore fat rpplylng It When the UNIT vrlue rppe~rr in the CMU rttrLbute, got NPIIt the UNIT frctor scorer the application GOOD if the artlcle 1s *rw and UNIT rppears in the CMU valuerr but POOR if thr artac~e rs w~heRr NP4 rpplisr esprcially Well to instancrs ih which units ara prermntt but does not apply 4t all If the hard of the nominal canatitusnt $8 a RE&# Stear In dlsceursd about warhlng mscninea and bleyc&quot;les, 'three rpeeds@nti~ht occur in m drdinrry way but for our curtpnt discourr~~ we do not antlclprte Such comblnrtlon, Certainly, we lo not expect Wthtee surface dl@Pl8C@IR@~tl', luch conrtralnfr rrlirvr the nr.6 for detailed rnalyrir.</Paragraph> <Paragraph position="3"> For Lnrtancer rrluar that thr @CoU#tLc mapper ham tentatively ~tf~teb both *SUbm~ttnaw 8nd ~UUblmrgad $Peedn IS a~0Ulfl~11lY pL1u8Lblr altrrnrtlv*r tar tilllng the gap in the parttally analyrra Phtr8e wtnr@@ -8 OL the U.8, Navy\ Thhl8 Is not ilpr~brblr SLnce arubnrrLnrr&quot; and nrUbnrrg~d ip@ad#* terembla arch ether in @any ways. They both rtatt with Vr*r their flcrt ryllrhle8 hrvr central vowelrl their last ayllablrs have high front vowel#) and so forth* If NP4 Is to be applird, however, the RELN frCtbr will resolve the do~b~t in favor ol w8ubmarlnen, and theta +ill be no nerd to teat In depth how we11 ulubmatged The UNIT factor of NP1& guider t'Ae choice between V&quot; and atti@\ where acoustic evidrnce tor 4 choice is typicrlly lacking* semmticell~y, qrl lrlamblrr wonsw in it8 ability to comblne with nUgber8 and unltap erg,# tan5 ma tonm, none hunbr@b\ hundredH, It the inrtancs ot the NOM Is mtonMr mfoot&quot; wkn~tw, ar Some other singular rxpresaion wlth the value UNIT tot it$ CMU attribute^ then mrVs judgsd to br more llKely than wthe&quot;, On the Pthw hrnbt it tho NOM la atuelM or VubmrrinrBst the artlcla cannot br T~U CHU attribute tot &quot;aw is (COUNT UNIT), which doe8 not intargrct wtth the value (MASS3 of the CMU attxlbute tot mfualwt the NBR attribute is (SG), which does not intersect with the value CPt) tor wrubmarin~UM, The factor8 r@ferenclng theae attrlbutsr rule out rppllcation wh4n the intersection Ir NIL, There ate typical ryntrctlc agreement ta$tte 41 longer phrare8 r built up# tha vartour attribute8 interact in other wry#, For instanc8t tht4 ryntactic pxaptrcier ot ralrtl~nal rwprrrrionr depend on which arpect at the relation t 8 prllacrrnt Ln an accomPanYLng preposltianal phrase, Prrpositional phrrs'er hrva the aftributrr of thclr NP objacti. Whrn a pr6positlonel phrare modlfirr a noun with the RELN attribute, thl CMU rttrrbutr tot thr rorurtrnt phrrrr ig drflnrd by trK$ng the union of the vrloar for tha two nomin&l constlf~ent~. As 8 re#u&tr phrrtr8 like urusL~rr Ulmplrerment of the brfaYettru have tkr VrZU8 (COUNT) ~nd those ilk@ Vutfroe dirplrc~er\f OL seven th808and tonsa hrvr the vrluc CCOUW? UWlTl, The dttferdnce in vrrurr mark@ the tact thrt the two exaapirr do not fit rltn equrr rare in a11 syntretle 4nvlronacnts. Xt is taterwzewi in thr UNIT and R~LN factors in (31 above, to LnfLUln~e the choice betwetn the two rrticlrr, which rrr srldom dirtinguishrd eler~iy by sound, The rule i8 tunad fa ptrfar %aa@ in the rbrence~ a# the UNIT vatuet rr In -the surrrce dlspl~~#mant of the Latryettcu~ end mra when it ir prrmrntr am in &quot;a 8urfaea df8placemlbnt O-f seven th6U11nd ton#*@ RA SUI~ICI dleplacutent ot thr &~L&y@tte% wrhteh impliar the porribility of havgng sort than one ruttaee diSPlaccaantt is ruled out carP&rtetsr, NPs rlro haye & HOOD atttibUt*r deri~@b from their initlrl canst~tuents. It 3 either declrrrtivq (DEC) 4s in Vthtt rob.rrinr% er WH-int*rr~uetivc (WHI rr in wunich rubmar &new .</Paragraph> <Paragraph position="4"> The WH valur 28 PtoPagrfed to thr lrrg8r PhtrSr8 in which NPI are con8tituent8, 8entrncea (Sl md uttrrrncrr [Uj t&lr the vrlur f4t their MOOD ettrlbutb ftem an lnitt~l WP, Our currant Voc.butaty doer not include Vetbr like Wknow@ and wtrllfl, which Cub embed %H QuaStionl likr @Do YOU knbw whit tha Iuttica dlrp%acamrnt LS?VQ~ ttha t imr bring, rr rcrusr that nantnitirl abon PnrdMlr rrr qot likely to have the vrlur WH tar HOOD. Echo qutstionrr *,ger ayb~ laid what?B ate not ruled ~Utp but have The convergence of many attribute6 at the hlgher level# ot Some of them are thown in (41, ATTRIBUTed</Paragraph> </Section> <Section position="7" start_page="22" end_page="22" type="metho"> <SectionTitle> MQObrFOCU8iCMUlREtN FROM #PI, AFFNEC FROM AUXBt FACTORS </SectionTitle> <Paragraph position="0"> TWEN POOR ELSE OK, AFFREG 8 IF MOOD EQUAL a(WN) AND AFPNtG EQ gNEC THEN BAD ELS~ U!$, RELN a IF- RE^ EQ WT AND CMU(NP2) EQUAL m[UNIT) THEN VERYGOOD .EfrSP: OK, PERShCR 8 IF GINTERSECT(PERS(NPt),PERS(AUXB)) THEN OK ELSE OUT; EXAMPLfES THE LAFAYETTE IS A SUB MAR IN^ (OK) THE LAFAYETTE 18 $UBMARINESr WHAT IS THEM (OUT) k UFAYETTE IS THE dUBHARINE (POQR) THEM ARE SUBMARINZBr rT AM A SHIP (OUT) WHAT XS IT' WHAT X& THE tENCTH (GOOD3 HOW MANY ARE WHAT (?,ObR) WHAT fBNeT THE .IURFAC& DISPLACEMENT (BAD) THE 8URFACE DXaPLACGMENT Id 7006 TONS (QERYGOOD); The PERSAGR (perron~agre~mtnt] factor trrtr Ear rgrsamcnt betwean the ro~crll*d pronoun8 and the auxiliary conrtltuanr The two grrnaiti~al carr tretorr, CCAdEl and CCAdLZ, rrqulrr thrt the $;~tclmnz&tf~&1 c8sar of the two NF8 rra not accu8-rtlve, Thcrt tridltianal ryntrctle agreement tests black applieatian of the roapealtten rule to putative axprerstonr like Yt rrr&quot; and @they S 'Them I@&quot; $S d~~bly bl~~ktdc Some of, th~ rr~rlning factor (tatcmrnta in 4) rtr lrsr trrdittonrlr On8 of th~re Is the AFFNEG frctor, which raferenear bath the MOOD &nd AFFRtG rttrlbutas @nd reducer the resrc Qrratly Lf the Anrtrnca ir purgortrdly r negative WH qurrtion Ifkr &quot;what Itnet the surfrec dl.tplac(rnent?a Canulna rcqutltr tor nagatlvr infsrsrtlon accur in highly tircumseribrd rltuatianr, Tha rh@taricrl questtan I8 not r qQnUfno raquert fbt Infor~btfbn [a,Qet wWh@ wauldn't ltkr to b@ rich cnd fanourtW), &quot;Who ltnCt herela is mhS~nabl@ only it thrra is rn a8tabltrhcd and limited List of paopla who aTe rxpeeted ta bc $rerant, ar in a elaroroon, @What isn't ye~t nrsel-rn =Where don't you live?-are patently Ub8U?dl The constraint on negative WH quartianr 18 arrrntially dur to Prcgritkc totcer aa we11 rr armbntlc onrr, 8lrniLlr forera arc at work in obrervrd tendencies for the first NP in the corvorLtlan detinrd by 53 to br indefinite in Faeur enly when the rreond one is also. Qtdbted ovrrrimply, in eohrrrnt dlrcour#r, the thing8 rltcrdy trrkrd rbwt-the %olw inforartlon-trnds to C~BI tir8tr What !. s pr@dicrt@d about if-tha wnaw'5 Intorrrtlon--tend# to fallow, Old infarmation ir Infarmrttan thrt ha& rlrrrdy been trlkrd abwt and eatablirhrd in the dircours~~ 14 that bt 11 lfkrlY to be ancabad in degLnit@ nsun phrases, Thcra ire likely to br In rubject porltlonr so that the oentrnca they intraduce Is conairtenf! With preceding Hntence~, Naw Sntermrtibn tandr to b introduced in Indefinite naQn phrase^, Tha next mantian of the Vmnc thingA will than ba 014 infarmrt$,onr 4Llplblr tot definite Paau8, Canraqurntl~~ uA Lafayetta is that bubmarlnsw racmt peeu1latr ralatlve to @That rubm8rlnc ir r tatay~ttu~, HA brtryettc is itn ia 6t111 mote pcbeul tar, Thrrs dlt~~~~~lrmba#@d ptpOk,abiLi#tic tsndencl8r lrr@ exprerre$ in the POCU8 factor at 83, The CMU att~lb~te~ al Pr~Vio~sly nBt@d, i8 not puw4'y ryntarctic, Qn the ather hwv!lr rnlrrttcrrlrr Ilks number agreement have rlway8 bean c~ntrrl to ayntrx, It it grxticol%rly intap@$tlngl th@r@f0rer that the numbar rgxaemant conrttalntr far 53 cannot be properly rtrtad wlthout rppaallng to CMU, To atat@ number rgres~tnt contfraintrt NS denoting unit% muat ba warked raparstely, dsntencer like *Theas ara a tubmatinem, nTThr8s Ir r tarpcdo tubam, wTht86 Lt nf$!Bll@ Irunchrrs*, end wThir are aubrR are clC4rly unqrammrticrl, and the ungaammatLcallty is ururlly attributed to tha fact thrt one of the can$t&tqtntr differ8 tn grammatical number from tha other two, Haw~vdr, &quot;The rurtace dl%pl&cement 18 rsvrn thousand tonsn la wnaAlY grrmmatic~1 @van though two ot the sanrt$tuantr are ringular and the third ir plutaA. 8ueh UIC of rrmantic rttributrr in ryhtactlc Lsctorr, paint8 to tihe conclu6ion thet the intcrgrrtlan ot Ilnfarmatlon faam dlffdr~nt rourea8 OF knawladge is well motlvrtad an bath l$ngUtttlC and haurLatle grounds, BIcruse ot the high frequtncy et HH qutrtionr in the protocols from which the V~Clb~la~y and phralc type& ware seltCt@dr the PG 11 nor tuncd to rxpcct them. A sentence dcflnrd bY 53 r@etlvt& i higher ScorQ troa the MOODi factor it its MOOD ts UH, This tuning can earily be changed without altering the ryntrx or senantics at the language, If the user bath @~tr&etl data tram the data bare tnel enters data into it, wfth n6 prodfefrble pltt+rn of altetnctlont Facfbrl lik) MOOD1 can riaply be removtd. A mete intrrerting alternative ;a to ra8et them dynrsicully In r diseaurrc context whara tha computer raaatlmsl askr quertlanr far tbr ur~r to rnawrr, Atter each Ular Qulrtlant the grraarr could be tumb to rxpcet r drelrrrtlve utterrne* who#@ gyntcx an6 ramrnticr wrrr rpproprirte and r@lavant,</Paragraph> </Section> <Section position="8" start_page="22" end_page="22" type="metho"> <SectionTitle> ABSTRACT </SectionTitle> <Paragraph position="0"> The semantic component Of thc tpecch understanding systgrn being Q~velopcd jblntly by and SBC rules out phrase combination8 that are not mtanlngfu) ~nd ~radueag semantic intcrgretatlong for eombin&tlons that area The system conslstr of a semantic network madsf and rou%lfl@$ that intaract with it, The net 1s partitioned into a set of hlorarehicalby ordered subnete, fdsilltsting the encoding of hlqher=~rde~ predlcstet and the maintenance of multiglc parring hypotheses, Composition EUaUtinttr mrnblning Utterance earnpanant% into Phrases# consult nctwqrk dQcctlptlong of prototype ritortiana an3 surface-to-deepcafe mspr, Outputr from th@r@ routines are network frnqmcnts canrixtlng of raver.1 rubnet8 thet In aggragatc capture the intcrrolrtia~rhipf betwean a phtr8ces syntax end rcmantlcs, Thi~ r@ocarch war supported by the Defsnsc Advance Research Projects Agency of tho Dcgartncnt of Defcnre and mon-f5torcd by the U.S. Army F18errcn Office undar cantrrct No, DAHCOI-75-C-0006,</Paragraph> </Section> <Section position="9" start_page="22" end_page="22" type="metho"> <SectionTitle> OVERVIEW </SectionTitle> <Paragraph position="0"> This prpcr describe8 arFtctr of the stsintic co~pQncnt af the rpccch undargtandlng systcr currently bclng developrd jointly ~y SRI and SCC, [Tor a ea~prchehrlvc discussion of nonacourtic partlonr of this sY&tomr tee WeLktr et al,, 1975,) The senantic eoQpQntnt conrlstr of two majar partrr a semantic network ccdlng a model of the trek domain and & batkqry of crmantic ca~~positian routines (SCRs) that art caardinat~d with the languag~ definition (roughly1 the ngrammarw for the eptcch undorrtandlng syttrmt gee Faxton and Rabintanr 1975, and ReblnsonI 19751, This paper cenctnttrtcs e%cluSiVely an the inttrplry b'atwecn these two major pact6 during parrlng. However, the rcarntic component also plryr importent roLcS in knorladgr managementr di@cour~c snalYsigr prrdlctLon, &nd question rn$narlng, An SCR jr called with n~twosk rcprerentatlsns of comPaments that the assacS~ted language dctlnitian rurc hag found ts be ryntactically capable at cornblnln8 to farm e larger PhFar@c UIIng knarltdge from the Semantic net, the SCPI adlminatc cambinations thatr although ryntactically acctptabla, do not meet scmantlc crltcria for msrningful unification, For eombinrtiant that are receptablrr the JCRI build network ~truct~rM ts reprerent the meaning of thr cosporitt phrarc, using the network rtructurer ot the camponant& ar building bloclcs, Thcae net atxuctwres QFO eonstructad so that (1) multlplc hYpothCSaS csncernlnq the proper lneorperatton af a given Utterance conrtiturnt fn larger phrrrer my be oncodrd rimultrnaourly in one netr (21 compttlng users of r canatltuent may share a single netrotk structure representing the constituentr and [3) tho assaciation between aaeh syntactic unlt at an input; and its translation image Ln the network Is cxpllcttly encoded for use In discourse analysis, THE 8EMANTfC NETWORK The ramantic network ie tha ptinclpal InfsIYhatisn source PO^ SCRIP cncodlng euch diverse rntltlrr as objectsr situatlansr ertrgorler, taxanomlra, dcfinltlonrl and quantlflad 8tdtrmcnt~.</Paragraph> <Paragraph position="1"> Network structure8 indtcrting portible telatlonbhl~s between objects rrc uacd to drttfminc the moaningtulnc~ss af ghkasc cambinskionor while tne network itself serve8 at the medium tar recording lnhrprdtrtlona of uttrrancc fragment8 during Parting, The ~ttucturt of this network differs fr~m that of canvrntionrl netr in that nodcr and srct arc ~~rtltioncd mta 8tg~~~~A, These 8p@c@gr Dlryln~ in networks tola roughly analogous td that played In strings by parenthcsarr group infornation into bundler that help to candanre and orgsnlzr the network*$ knowltdg~~ An introductlan to net partltionlng 1s provided allawhetr (Hrndrix, 19351, An 1lluatrativr portlan of the permanent knowledge secttan at thr rrmintic network 1s depicted in Pipire i. In the uppar left cbrnsr ir.ndda 'Uor rrprr#enting the univbrral set U, Ta the right 1% nod* QPHY80BJ$*, rrptalantinp the rat PHYdOIJB of FIGURE 1 A aFAIMPLiNO FROM THE GENERAL KNOWLEDGE NET physl~al ~bj~eta. That PHYSOBJd I8 1 sublet of U 11 lndicrtlld by the smart from 'PHY80BJ8* to 'U'. A subset of PHYBOBJS is SUBS, the set of all rubmarinss, A particular elemant 02 SUB8r as indicated by the r~are'lrom *DOLPHIN@ to 'SUBB', is the DOLPHIN.</Paragraph> <Paragraph position="2"> The DOLPHIN is r DartltfPant In r Qattlcular situation, HB, the rlturtion in which thr DOtPHfH has a beam of i9 feat. HB is rn clrmtnt of <HAVE,BEAMBP the set of all rlturtionr in which a physical object is chrractarizrd by a rnrarurc at itr breadth, Cartrin autgoing arc# fro% r nods rsprrrrntlnp r rituatlon are ured to specify rbtuatlon rttributrr through derp rrmrntlc crtcs, lor txrmPLe, the outgoing obprrc from 'HB' rveettirs the value of the wobjw [(ObjftCt) lttrlbutt ot HB to be DOLPHLN, Hera@tttr the notatton a~~ojw will bt uled to indtchtc hthr vrjoe ot the attribute (Q) Obj** The network of Fluure 1 hat bran divL4e6 into five spacer, KSP S4r 85, 86, and 87, Pictarirllyc each of thrle 8paeCa 11 tdOreatnted by' boxc The mo8t global informrtion In the network ir encoded in rprca KS rthr outerrnofit bog, ramrtimrs erllQ4 the wKnowledqa Spacea) which Includqs Such ~n~tltirr rr node8 'IJ' and 'PHYBCJBJ6' and thr r-arc connrctlng them. Thr baxar rrprerentinp #vrcrr 84 thraugh 37 may be thought of as hole8 ln the box of KB, Patrllaling the rrlrti6nahlp botweqn an inn~r and an outer black of an ALGOL program, qach of thrm sprees 8~ecitle8 a nore lac41 rrra of the net than 18 rpeciticd by KS. From the pcrrprettvr ot 85, far aXamDlr~ it &S Pasribls to accrtr bath lacrl node *Po and (rtlatl.vely) Pl~brl node .PHYIOBJIF, Haravrt, from KI the nodal and rrct Inride $5 ate not &cc&##ibl@, The hierarchy of SPlca 1oealiertlon nay be ttprerrntcd by r partial ordering suoh rr that of Flqura 2, From any FPICC 251 the node8 and arcs arc aCC*LISblc that lie in 6 or fh any mace S' above 8 in tht hierrrchy, For rxawpler from 83 onty nodes and arcs in 53, 62, $1, and KS &?Q &ceersfblc, Pictorfrllyl kt may ba nscegaary to 6raw an arc crossing box baundariar , In such erars, the arc belbngs to the space (or spacer) In whose box tht ,arc Zabrl is written, Sp&~et may overtap, For exampler in figure 1r node 'ED,HBe ller In both rpacc 84 and rpaeo 85, Further, r spate may tcrve 4r a node in s more global Spice, Both 64 and S5 bhhsve &I nodes in KS and arc connected by s conssrsrc (~anseq~rnce]~ FIGURE 2 SPACE LOCALIZATION HIERARCHY Typleally, locrllzed rpaeQ8 ruch ar $4 and 55 art used to @ncoda higher-order &quot;~rtdicrte~,~ such as quantiflarrr logical eonnrctlvrr, and hyp~thetic11 data, Here, 84 and 55 rm used to ancoda an fmpllcatlon, Thr rpaet 84, doubling a8 a node in apace KS, Lo connected by an emarc to '*fHPtY>' and by a eanrwrrc to 'SSPr The inttt~rttati~n of any tiamant of set cLM~ty.> $8 that if rntltlos can be found matchlng the structure of the element Egticer then the exirtence of rnttties matching the rtructurc of tho rrsoclated con14 Wac@ may be interred, The only StnI~tUm encoded In elomrnt Speec. $4 la a nod@ PED,#B0I with an @-arc to QcHAVE,BEAM>@, This structure mateher any concrete instance of eHAVE,BEAM> (such as HB), Thus, fat any instance of (WAVE.BEAM3, entitlss matching thr tttuctore of 55 JnUIBt rxlrt, The Stru~tuxe of 65 indicates that the element ot cHAYE,BEAM> will have a IQobj, rhlch is an tl~mcnt of PHYSOBJSI and a #@mcarurt, whlch is rn element of LINEAR,MEA8URES, The ImPlication mcQd1)4 b3 $4 and $5 arervclr to delineate the 8@t GHAVE~BEAM~, Thrt Ia, the im~lieatlon lndlcatar all the attribotrr tdrrp ear~sl of r gHAVE,BEAM> riturtion and their r@nWr of 4e~1ptrbla vrlu~~, Thlr d~llnrrtlon may ba urrd during parring to teat the plrutibiAity of r given group of rntitirr baing united in r (HhVE,BEhM, situation 6rr in a prcdlctlvr mode, ta rUug@rt &o#glblc rdntance partlclprntr, Such delineation8 arc mcadrd for every rltuation and want rat known to the system, a recond cXsmPla in Figure 1 baing the drlinration at rrt *BUILD>.</Paragraph> </Section> <Section position="10" start_page="22" end_page="22" type="metho"> <SectionTitle> THE SYSTEM IN ACTION </SectionTitle> <Paragraph position="0"> The urs of She SCRs and remantic network in translation may be seen by conriberltlg the parr.Sng of *The paw- plant at the sub was built by Westlnghouserm The Ultimate result of the translation Process for thts utterance is the network structuge recorded in the SCRATCH $pact of F.1uure 3, StrucEurar repre8enttng new i~puts arc construeted in a scratch space (or &pacer) to prevent them from becoming confured with the Sy8twnq% Petmr~nt knowledge (recordrd in K8Ir Slnco the syrtem unclerrtand8 new l~putt by appaalfng to PY@V~OU~ knowledge, thare art many link81 In the form of cmarcs, from the SCRATCH Space Into KS, (Note! Only a fragment of KS it Sh6Wn in the varlous flouras of this paper,) FIGURE 3 PARGE TARGET STRUCTURE FOR &quot;THE-POWER-PLANT OF THE-SUB WAS-BUILT</Paragraph> </Section> <Section position="11" start_page="22" end_page="22" type="metho"> <SectionTitle> BY WESTINGHOUSE&quot; </SectionTitle> <Paragraph position="0"> The interpretation of the network in the SCRATCH space I8 a8 follcwr: Node '0' reprerant8 an element of the ret <BUILD> of building event8 an wnAcn a #@dgt W built a #%bj PI The agent W of thr building event is an element of the rat ot WESTXNGHOUSES.</Paragraph> <Paragraph position="1"> The #@obj built by W is Pt an elemant of the art POWER.PLANTS, According to node 'H*, thia power plant is the $@subpart in a <H-AVE,PART, relrtionahlp in which SI the PartlCular mqmber of SUBS currantiy In context, is the #@su~p.art (Wtrp8rt)a Discourse analY8Ls mrchani#mS dlrcusred Ln Deutrch [1'975f and, rare fully, in Walker rt al, (1975) will br used ta rrraclate W with the unique Wertlnphb~rr Cerporatlon known to the semantle net in space KS, The other definite NPS (&quot;the sub&quot; and Vthc power plant ot the rub9 vwill Llkewira ba r@~~lv~d, ToesUpp~$s 8sCOndary details whtle considering the building of this structuret rrrum~ the highly airn~llfiad language In the trrnalatlon procerrt rpacer are created to reprarent the ternantleg of each grammatically defined conrtltuent of tne total utterance, These spaces Ire shown In Figure 4 with he8VY arrows indicating the space hlararchy, athe-powrr-plantc, an 8CR ir called to set up a rpaee, NPI, below KS tn the partial dtderlng, Withln thfr rpace, a structure is crested reprerenting the meaning of Hthe~pawer-~lant~, 8lmilarlY~ new space8 are bet UP to encad@ the 0thN Sentence cbnrtitucntr that correspond to cxpllcit lexLcrl antrlas, A8 the ~hrrar gtbu~s rubphrase8 into lakper units, SCRr arc ~~llcd to aid in the praters, Usinq rule R4, PREPPl (MbyR) and NP3 (@WestingMu~c~l are combined to form PREPPl C ''by Wcstinuhousew). PRePPl is allu%ccrted it8 gwn $pace, out no new rtructures are created rtthln it.</Paragraph> <Paragraph position="2"> When ryntaetic considerat ions suggartr combining VPk (wwa.r-bul~~tn) with PREPPI, the appropt. &ate SCR it cac.icd, Con8ulting a rurface~to~desp*cast map aosoeiattd with the lexlcal entry fat he verb wb~lldR, the 8CR dttetmknt8' that I @byw PREPP following the verb often rignalr the drrep rrqt ear* Iri a plrsrlvc conrtruction, Operating under thir hypothesis. the 8CR checks the voice Of VPI, PISSing thir taltr the SCR naxt cheek8 the stmantic fearlbiLlty of the NP of PREPP1 serving 4% tne bQagt in a eBUIbD> event, To $0 this, the SCR Consult8 the #@d@lincstlon of tBU13LD> in 8pQcc KS C14~ Flgute 1). The dclinration 18 tncoded ra an <IMPLY> 81turtlon in tatma of spacer 86 and Sf, A8 dlteurrrd crtllerr this drlinertlon indieate8 that any #@rut ot a <BUILD% situation wurt be an altwent of LEG&L,PERsONS, The candldatr for the #@apt position is W of space NP3. Sinec W I# an alcaont of WEbTINGHOUsES and UESTINCWOUSES ir a subsst of LEGALcPERSONSt W in accepted, A canstruction such ar wbuilt by the rybmrt~ncm would have been rejected.</Paragraph> <Paragraph position="3"> Once VP1 and PREPPl have paS8ed the acceptability ta~t~, a new #pace, VP2, fs constructed to encode the rarultsnt VP, This new rpace link8 node '0' of VPI with node 'W* of WP3 via an agt-~~rc , This new arc L8 accerslblo only from space VP2 (and lower spaces in the hierarchy) and La not rect~aiblt from either VP1 ox NP3, Thtg leaves ths com~onents encoded in VPI and NP3 free to comblne in alternativtr to VP2 if need be, continuing the Parse, NP2 ('tha~SUb&quot;1 ia comblnrd With VFZ (wwar=built by We$tinghoure~.) to form, sl, 6fter parrtng trrts ~imlhr t& thebe above, The obj~alc linking the constituent phrart6 of 81 ir aontalnad Ln space Sl and hence Is Inacc~srlble from the spaces of the conrtituentr, Notice that t construct &quot;the-~ub war-built by WaStlnghoUsaw whlch is encoded bY 81 i# r rpurious intrrprttatioa ot uttcrahcr companent8.</Paragraph> <Paragraph position="4"> UIinq rule R4r PREP may be eambined with NP2 t~ form PREPPZ, Th. n.t*brk itruCt~fe8 ~cc4ssibla from PREpP2 do not include the ispurlou8S obj-arc f.rom '13&quot; to '8' that llsr in space I When the syntax at rule R2 rupgrrtr cunbAning NPI and PREPP2 to form r new NP (wthe-p~w~r~pl&nt of thr*rubw), an SCR is called, This SCR checks NPI to $40 if It is relational In nature us Is 'bermH in *beam Of the DolphinV and hence rwprcting an argument to b4 8UPPl$,&br Since NPi fall8 this ta%t, the 8CR check8 the proprrtirr of the PREP *ofw and d28covet# that it may be used to encode <HRVE.PART> rlturtl~ns. Calling upan the dellneation of <HAVE.PART> and a~proptiata #urtaea.to-dccp~care maprt the ICR detetmlnas thic to be 4 legltlmate interprctrt'ion and hence build# space NP4 with a node 'W' and three arc8 at shownr Whlls these new Cenrtructt 4t4 4C~ddslbLe from space NPl, they ere inrccrrrible from conatltuantr NP1 and PREFP2 (and NP2f. Furthermore# they Cannot be accerred fram ~puxlsur #Pace Sit hence the construction sf NP4 hag not altered the view of the not from 51.</Paragraph> <Paragraph position="5"> U$ing rule R1, 52 is canStruct@d from NP4 and VP2, In addition to the QbJ-atC contrlned in space 82 it$clft the view of the net from 82 includtr all the infarmatlan acCtodibla from either tpuce NP4 or rprca VP2 and hence is idantical to the view from apace SCRATCH at Figurc 3. Slnca the ~arae mrrsrpon@lng to space S 1 doe8 not succ4rrfully account for the fragment uthe-power-plant ~f'r it 11 rbje~tadt and S2 is accepted rr rxpEb!rring tha meaning of the inputr The partial ~rd~ring of spacer fram S2 to KS indlcatrd In Figure 4 1s idcntlcrl to thrt rcprraentcd more elearly in ~iqure 5, which, brcauae of the choler of $pace labclq, may bc rrcoani~cd rr the parse tree of the input rentcnce. The syntax of the input and the assoeiatlon between each ryntactlc unit and its corresponding rcnantlcr has thtrefo?~ been captured in the structures built by the SCRs,</Paragraph> <Paragraph position="7"/> </Section> <Section position="12" start_page="22" end_page="839" type="metho"> <SectionTitle> DISCUSSION </SectionTitle> <Paragraph position="0"> Paxtitlaning is a recent lnnev&tion In rrmrntic nrtworkr, Aa shorn lbOV@t this new fe&kuI's enabler networks to maintain altt&nat~ve hypothe&e# (a,glt 81 and &21 concerning tha uor of Utterance c~n#tltU@ntr and enrblc8 such competing hypotharta ta rharc network 8ubprttr (a&?,, VPZ), Wlthout prrtitlonlnu, the pack-linked nature af netwotkr caurar a constituent to be rltetsd when it 18 incorporatad into & larqer unit and hanca renders it unurrblt in e1taa;nctLvc conrtructianr, The highly rmbigu~ur naturr of WoU8tiC input mrktr thest ablLit1W to maintain Partitleninp also allows rrlcctrd portion6 of a network to be arroclattd with ryntretic units, shoring 'the correspondtncc bttwtrn network cntitttg and the syntactic strocturrr that *@re used to communlcat@ them, A& dlrcussrd In the section on rs6ociatlon i6 crUcf81 in analyzlnp the tlllptie uttcrrnctr that arc 60 characterlrtic of rpecch, This paper presents a formal ism cal led Semantic Processing Scheme, SPS, for use in describing semantic interpreters. SPS is a rule-based system with a rule-ordering scheme that can produce deep case structures from phrase-structure trees. It was originally developed to demonstrate how English prepositions, such as &quot;up&quot;, &quot;down&quot;, and &quot;through&quot;, which reference location, motion, and orientation in space could be semantically interpreted. This paper presents SPS in its current form and shows how it can Landle these prepositions, call ed the locative prepositions. SPS is continuing to be used in studies of semantic processing.</Paragraph> <Paragraph position="1"> Computational linguistics has seen a considera'ble amount of work on the development of general model s for 1 anguage-unders tandi ng sys terns. Among tile 4 5 7 most we1 1-known examples of this is the work of Schank , Simmons , Wlnograd , and Woods . On the whole, these rnodgls have been tested on broad but shallow subsets of ~nglish, in that they have been applied to many different phenomena but few extensively. The authors of this paper are taking a different approach. We are studying a few phenomena and attempting to allow for them in considerable detail. At the least, this approach should lead to better treatment of the particular phenomenon.</Paragraph> <Paragraph position="2"> It can also lead to the development of new general models or the revislon of old ones.</Paragraph> <Paragraph position="3"> The paper is written in five sections. The f4rst describer the overall Interpretative framework. A second indicates some of the difficulties inherent in the processing of locatlve prepositions. An overview of SPS Is glven in the third section. The last two sections expand on the SPS description and discuss how the locatives are all owed for.</Paragraph> <Paragraph position="4"> Syntax, Semantics, and Pragmatics. SPS is developed for a traditional three-level system, with syntactic, serna~tic, and pragmatic stages. Based on the level of abstractness, these stages compare most closely to Yinogrd't and Woods'.</Paragraph> <Paragraph position="5"> The syntactic processing stage is assumed to take strings of text and produce underlying syntactic structures in the form of cansti tuent structure trees. We are attempting to keep these as close to surface eonstftuent structures as possible. However, some divergence from the surface form is currently assumed. For exampl e, imperatives, Interrogatives , and relative clauses are assumed to be shown in a declarative-li ke form, and preposi ticns are assumed to have their complement immediately following then.</Paragraph> <Paragraph position="6"> An SCS based interpreter takes these syntactlc structures and produces output whlch ref1 ects underlying semantic structures. The form of the semantic structures is also a topic of our research. We are uslng Case structures *2'4v5 and PI anner-1 l ke assertional forms . It 1s Interesting to note that our results to date tend to indicate the need for a level of ahgtraction somewhere bet~een Simn 'IS and Schank's semantic nets. In developing the semantic lewl-, we are trying to make it the one where &quot;general knowledge of language and its relation to the world&quot; is applied. This is in contrast to the pragmatic level, where situation-specific information is used to interpret the semantic structures. In sumnary, a system employing SPS would construct syntactic trees, use SPS for the production of Case structures, and employ a pragmatic processing scheme to interpret these structures.</Paragraph> <Paragraph position="7"> Problems in Processing Locative Prepositions. Part of the problem with the semantic interpretation of locatives is the complexity of the structures necessary to represent them on the underlying syntactic and semantic levels. 'This section discusses these problems and introduces our semantic structure notation.</Paragraph> <Paragraph position="8"> The representation of locative prepositional meaning in Case structures has been problematic. The number of cases that Fillmore has postulated for them has risen to four--Location, Source, Goal, Path. He a1 so features locatives in a paper on problems within Case grammar . The worst of the problems involves not being able to interpret the semantic weight o.~ meaning of the representation. An example of such a probl em comes in the represen-I I tation of the following: &quot;Bill held his daughter on his lap in the tunnel. , Both of the locati* phrases w~uld be assigned the same case - Location. Howeverj they actual ly locate different objects.</Paragraph> <Paragraph position="9"> Bi 17 's daughter was said to be on his lap while both of them were said to be in the tunnel.</Paragraph> <Paragraph position="10"> Similarly, the use of an unordered set of cases fails to a1 low for the difference in meaning of the following two- sentences, where the first two prepositional phrases in each would be in the Path case: '!He went down the hi1 1 across the bridge to the chapel.&quot;, and &quot;He went across the bridge down the hill to I I the chapel. .</Paragraph> <Paragraph position="11"> The Case representation we are using deals with these problems. This representation uses only one case for all spattal references. This case, the Place - case, identifies spaces which derive from the location of participants i.n its action, event, qr state of affairs (or event/state). Which participants and how each space relates to them depends on the type of event/state, The basic structure of the assertional notation can be seen by showing how a Place case wul d be represented: ( :PLACE #E/S $PO). The &quot;: &quot;</Paragraph> </Section> <Section position="13" start_page="839" end_page="839" type="metho"> <SectionTitle> II II </SectionTitle> <Paragraph position="0"> identifies a relation, the # an event/state, and the &quot;8&quot; objects (note that many of these will be replaced by variables in the actual assertions produced). The first element of any assertion is always a relation, which forces interpretations on the other elenients. With the relation :PLACE, the last two elements must be references to an eventlstate and a spatial object (space), in that order. The specific spatial objects that are referred in Place assertions are call ed Pl ace objects.</Paragraph> <Paragraph position="1"> The prepositional elements on the semantic level can relate Place objects directly. An example of this is the representation of &quot;She died away from where she 1 ived.&quot;, i .e., (:PLACE #E/Sl $PO1 ) (:AWAYFROM $PO1 $PO?) (:PLF\CE iiE/SZ bP(12). here a prepositional element relates the Place object of the two event/states corresponding to &quot;she died&quot; and &quot;she 1 ived&quot;. Prepositional elements can also relate spaces derived from Place objects.</Paragraph> <Paragraph position="2"> This is seen with the representation of motional meanings, such as in the mu1 tiple Path sentences above. The Place object of &quot;go&quot; and other notional event/states are taken as indicating the space traversed by the moving object or objects. For the example sentence, the Place object would show the space through which the person travelled. This is acceptable since the static positioning of these spaces (or paths) as &quot;across&quot; the bridge is logically equivalent to his going across it. The predication of derived spaces arises in the handling of the ordering problem. The motional Place object can be taken as composed of parts that are ordered like the parts of other objects (from front to back or top to bottom). The ordering here is based on the time the component spaces were occupied. Using relations to select segments of the path and the end points of these segments, simple mathematical relations compare the orderi ng of the component spaces, coirpari ng parts of the journey in time. A semantic structure might look 1 i ke the foll.wing:</Paragraph> <Paragraph position="4"> The Place case proposal avoids problems 1 i ke that with the Location case exarnpl e, through the representation of certain syntacti cally simp1 e clauses with more than one event/state. The representation of &quot;He held her on his lap in the tunnel .&quot; shows an event/state corresponding to &quot;he held her&quot; and one corresponding to &quot;she was on his lap&quot;. These are constituents in a causative event/state, with the first causing the second *Fillmore roves in this direction in 121 Similarly the representation resembles those of Rurnel hart and Norman 3 and schank4. We Wave attempted to systematically work out the event/state analysi , as far as it concerns locatives, for all verbs taking locative objects. 8 This complex structure solves the case problems by a1 lowing each preposition to predicate a different Place object. &quot;On his lap&quot; predicates afi existential event/state showing where the female was located. &quot;In the tunnel &quot; can predicate the Place object of the causative event/state. The interpretation that space is that it is composed from the Place objects of its two constituent eventlstates. Hence, both peopl e wi 11 be predicated by i t.</Paragraph> <Paragraph position="5"> While these last two devices enable us to avoid representational problems, it should, of course, be remembered that semantic interpretation must support these forms.* Tied in with semantic complexity is a1 so complexity on the syntactic 1 eve1 . Assuming sentences are normal ized in underlying syntactic' structures as specified, locatives appear in four positions: as the qualifier of a head noun in o noun phrase; as the compl ement of a copula; as the adjunct to a clause; and inside a clause as a locative o,bject. The adjunct usage can be differentiated from the locative object by its tendency to give overall predication to the event or state referenced by the clause. In &quot;He held her on his lap in the tunnel .&quot;, the first phrase is a locative object and the second is an adjunct.</Paragraph> <Paragraph position="6"> To summarize this section has presented a variety of points about the semantic interpretation of locative pr'eposi tions- that they can require complex case representations, and that they appear in a variety of syntactic environments. SPS has been designed to relate the syntactic to the semantic *There are other phenohena for which the Place case proposal a1 lows. The co'mpl ete representation is descri bed el sewhere.6 What has been given here is enough to show the difficulty of interpretation.</Paragraph> <Paragraph position="7"> environment of locative prepositions. How it deals with these problems will be described after a brief over vie^ of the formal ism, SPS. The SPS formalism is mst closely related to a fam'ily of semantic interpretatian schemes deriving from Woods' 1968 The close similarity to that work 1 ies in the basic form of rules. These rules have the form &quot;pattern + action&quot;, where the pattern side specifies tests to be made on the syntactic structures, and the action side specifies forms to be added to the semantic structures. The tests are mainly based on the matching of tree fragments against syntactic structures and the testing of semantic features associated with those elements matched. In SPS, sets of features can be directly examined or compared to other sets of features. Each lexical entry may have mu1 tiple sets of features associated with it. SPS a1 so a1 lows these tests to be made against features associated with registers by other rules.</Paragraph> <Paragraph position="8"> If the tests are successful, the action element is executed. This principally adds assertional forms to the semantic structure, but can a1 so set values of registers. In the assertional forms, means are provided to a1 1 ow references to the syntac4ic constituents and 1 exical entries matched, as well as to other forms through the registers.</Paragraph> <Paragraph position="9"> SPS uses a finite state transition net for ordering the appl ication of rules.</Paragraph> <Paragraph position="10"> Each noun phrase and sentence is analyzed under the control of a net associated with it. The process of forcing interpretation through constituents is guided by marking completely interpreted nodes. The overall tree is processed from the bottom up.</Paragraph> <Paragraph position="11"> SPS Rules and Locative Prepositions.</Paragraph> <Paragraph position="12"> To see how SPS works in detail, and ,,&quot; explain how it allows for locative prepositions we look at a typical rule:</Paragraph> <Paragraph position="14"> This is a rule that might be applied to interpret the prepositional phrase in the sentence &quot;He held her on his lap.&quot;.</Paragraph> <Paragraph position="15"> The rule is identified as 2-STAT-LO. This particular name indicates that it deals with a preposition with a certain static type of meaning (2-STAT) used as a locative object (LO). The pattern portion of the rule consists of two parts. Tbe first describes the syntactic environment in which it applies, while the second gives the semantic feature tests.</Paragraph> <Paragraph position="16"> The specification of the syntactic environment is done through reference to tree fragments that must be matched in the syntactic structure in order for the rule to apply. The reference is made through the asterisk-numberdash-1 i teral fons in the rule, e.g., 11*1 -S5I1, where the 1 itera1 s identify fragments such as the following:</Paragraph> <Paragraph position="18"> These-fragments would match a locative object use of a prepos~.~lur~ arlu LH~.</Paragraph> <Paragraph position="19"> verb of that sentence. Other fragments are needed for other usages. The two forms in the rule after the reference to the first tree fragment will be described in the ~ext section.</Paragraph> <Paragraph position="20"> The second part of the pattern side is a set af triples used to test semantic features. These tests are of two types, EQ and COMPATIBLE. The EQ or &quot;equal&quot; tests ascertain the presence of a single feature in a set. Its first parameter is the feature and its second the set. The primary use of this test with locatives is to identify the cases where the prepositional tree fragment has actually matched a locative use of a preposition, since the syntactic parser can only be assumed to identify prepositions and not differentiate their senses. SPS allom for this discrimination by providing reference to the 1 exical entries associated with a preposition .* These references are made thro+ugh the number-dash-number forms where the first number refers to the number associated with an occurrence of a tree fragment in a rule, while the second refers to the leaf number in the fragment.</Paragraph> <Paragraph position="21"> The COMPATIBLE test is meant to allow for the semantic co-occurrence restrictions. It takes two sets of features as arguments and evaluates to true if the sets share at least one element. The above rule il 'lustrates how this test can be used to allow for three types of restrictions affecting locatives. These are between a verb and its prepositional object and between a preposition and the two elements it relates (Winograd's semantic subject and semantic object).</Paragraph> <Paragraph position="22"> The fact that SPS allows three sets of features to be associated with lexical entries is used for the three restrictions on 1o.catives. One set, accessed through number-dash-number , is for restrtc tions placed on the *With ambiguous entries, SRS tests each sense individually , therefore, any of the lexical references can be considered to have a unique meaning at any one time.</Paragraph> <Paragraph position="23"> preposition by the verb. The other two sets, identified by the OBJ and SUB prefixes are for restrictions on the elements related.* The final triple in the pattern differs from the othprs in that the test is against a register.</Paragraph> <Paragraph position="24"> SPS allows for registers that can have sets of features associated with them. The registers provide communication between rules to a1 low for some contextual effects. Tests may be made against registers both before and after they are set, with the test held in abqance in the former case.</Paragraph> <Paragraph position="25"> The use of the register here is to identify the semantic subjest of the preposition. This is necessary since it can not be imediatelv said where the subject is situated in the sentence. In the following sentences it is initial, median, and final: &quot;He held onto the rope.&quot;, &quot;Hk held her on his lap. &quot;, and &quot;He held in his hands the letter I sent Mary.&quot; Given that everything is successful on the pattern side, the action side is executed. An example of rule application is given below: the element in the object case, and that the 1 i terals beginning with . are *Note that the test using OBJ is on a noun phrase. At the moment SPS takes references to noun phrases and sentences to be to the lexical entries of their head noun and verb, respectively.</Paragraph> <Paragraph position="26"> variables representing some event/states or objects.</Paragraph> <Paragraph position="27"> The purpose of the rul e is to relate the location of the object being held to the location of the complement.</Paragraph> <Paragraph position="28"> These locations are available through event/states which identify where each of the two objects were. We use the predicator $BE for these event/states, such as in the one for &quot;his lap&quot; which is produced by the rule. Hbw the correct assertions are produced from the assertional forms is illustrated in the above rule.</Paragraph> <Paragraph position="29"> All the direct references to relations and objects that start with &quot;:It (I tl 'I#&quot;, or $ are inserted directly. The number-dash-number forms provide a reference to a 1 iteral stoAd in a lexical entry . For prepositions this 1 iteral gives the physical relation that the term refers to.</Paragraph> <Paragraph position="30"> The two Place objects are formed by the use of a variable generation feature using the &quot; !XIt'-number-&quot;)&quot; form. References to the $BE event/state are also formed in this way. The other event/state is referenced through a register. SPS allows registers to hold variable names as well as feature sets. The register used here must be set with the variable name used when the event/s tate was const~ucted.</Paragraph> <Paragraph position="31"> As the above example shows, the registers are used here in situations where mare than one event/state results from a clause. When only one event/ state exists, a simple reference to the major covlsti tuents of a sentence is necessaty. SPS allows for this by automaticall v associating variables with the S and NP nodes in trees. These are referenced through forms like &quot;!I-2&quot; which here gets the variable associated with &quot;his lap&quot; (presumably !X4). This variable will also appear in the assertions describing the object; hence co-reference is achieved* A facility of SPS missing from the exampie is register setting. Two operations can be accompl ished. Either a variable is loaded, or both a variable and lexical entry are loaded.</Paragraph> <Paragraph position="32"> These registers are essential to the development of the complex structures that must be produced at the semantic level. Besides he1 ping produce mu1 tip1 e eventlstate structures, they also provide the means for ordering the partial predication of a path.</Paragraph> <Paragraph position="33"> In any list, the variable identifying the location of the last mentioned space can be loaded in a register. Then with the next phrase on the 1 ist, the variable can be referenced to form the comparison. The new f'inal value can then belodded in the register.</Paragraph> <Paragraph position="34"> The Ordering of Rules and Locative Prepositions. The SPS system appl ies its rules in a strictly orderea fashion. Major constituents have rules appl'ied to them on the basis of an ordering shown by a finite state transition network. The following is a hypothetical network for ordering the application of some rules:</Paragraph> </Section> <Section position="14" start_page="839" end_page="839" type="metho"> <SectionTitle> +O Initial State R4 >@ OFlnalState </SectionTitle> <Paragraph position="0"> The 1 iterals on the arcs name rules that must be successfully applied be'fore a state change can occur. These nets are set up for noun phrase and sentential elements, and are used with a marking scheme such that interpretation, of a constituent is complete only wMn its net is in a final state and all its constltwnts are marked as interpreted These nets are set up for e,ach head noun or verb interpret noun phrases and sentences.</Paragraph> <Paragraph position="1"> Their utility is in allowing for the orderings among case elements. The constituents fill ing semantic rol es in sentences can only appear in certain positions with respect to each other. This is particularly true with respect to verbs since the roles and orders differ from verb to verb. Hence, the net used depends on the head noun or verb. There would be no need for a net if the number dP constituents were strictly 1 imited. However, with locatives there can be no 1 imi t on the number of intermedlate points or on the syccessively finer specification of location, e.g., &quot;He lives in New York near the Battery by a park.. .&quot;. Nets, wlth their ability to loop, are useful for these structures.</Paragraph> <Paragraph position="2"> Interpretation proceeds from state to state until success or inabil ity to progress further. In the latter case, SPS can back up to the last state that still had rules to apply, a fact useful in allowing for pemtnic ambiguity.</Paragraph> <Paragraph position="3"> Reglster tests have been mentioned as bejng postponed until the register is set. It could happen that the register never gets set, e.g., &quot;He hits into the stands.&quot; does not specify what went ihto the stands. This is a case of semantic ellipsis. SPS allows default condit4ons to be associated wfth registers that are left te~ted but unset.</Paragraph> <Paragraph position="4"> The maans of progresslng through a constl tuent and assurlng its complete Interpretation Is provlded by forced anchor! ng and marking schemes embedded in the rules. An example of each is seen in the rule shown in the prevlous sectlon, i .e., &quot;*I-S5 (1 2 3 4) I(4)&quot;. Both schemes refer to nodes in the tree fragments uslng a preorder - root first, then subtrees left to rlght. The numbers in the parentheses in the example rule refer to nodes of S5. ?he anchoring scheme restricts these nodes to being matched to the 7 eftmost uninterpreted nodes in the structure being processed. When a node is prefixed, by &quot;I&quot;, it and the nodes it dominates are marked if the rule succeeds. tjence; the example rule marks the prepositional phrase as interpreted.</Paragraph> <Paragraph position="5"> Because of this marking scheme the noun ~hrases and sentences of a tree are interpreted from the bottom up.* Conclusion. A formal ism for writing semantic interpreters, SPS, has been described, It alfows for a semantic feature scheme that can describe the restrictions on locative prepositions. SPS also has registers that can be used for these restrictions and for building up the case structures that represent the meadings of locatives. A rule-ordering scheme is also heloful here.</Paragraph> <Paragraph position="6"> It car] be said that SPS is a good vehicle for dterpreting locative prepositions, and that any system for semantic Interpretation with these features will be able to analyze locatives. We do not claim that SPS is a cornpl etely successful semantic interpreter. However, the formalism seems to be clear and expressive and it does work for locative prepositions which, to the authors' knowledge, have not been as effectively dealt with elsewhere. It could well provide the basis for a uniform, coherent structure for semantic interpretation, especial ly for Case analysi-s. The authors intend to continue to experiment and develop it as a tool for language understanding .</Paragraph> <Paragraph position="7"> SPS is implemented in LISP 1.6 on the DECSystem 10.</Paragraph> <Paragraph position="8"> %re detail on a somewhat earl ier version of YS can be found in Chapter VII of [6].</Paragraph> <Paragraph position="9"> Acknowledgements. Thanks are due to David Brown and Roberto Pardo for their reading of the paper and early programming work, respectively. The guidance of Richard L. Venezky is a150 grateful lg acknowledged.</Paragraph> </Section> <Section position="15" start_page="839" end_page="839" type="metho"> <SectionTitle> ABSTRACT </SectionTitle> <Paragraph position="0"> Varicus representations have been used to partray the ribmiqs of mrd (mtably action) mncepts. The mst pruninent of these include decanp3sition trees, 1- repreentations such as the Predicate Calculus, and senantic netwrh. The propsition-based semntic ne-rk notation developed by Schubert (1974) is especially well suited for including praptic and sepantic Momtion as part of the meaning representation of irdividual wrd mncepts. The attempt is made in this paper to explore the nature of mrd concepts dse mxmings are represented as senantic netmrks and ,W .investigate their cmpu~tional use within the fr-rk of a natural language prpcessing systan.</Paragraph> <Paragraph position="1"> The meaning of a cxmce1ps. is explained in lx%rns of other concepts and thmug its relatimship to other concepts. Various representations have been used to prtray the reanbgs of corrcepts. The mst pranhen~t of these include deccmposition trees (Idcuff, 1972; Wilks, 1973), linear represenbtipns such as the Pre dicate CalcuL21~ (Sandewall , 1971) , and mtic nemrks .</Paragraph> <Paragraph position="2"> Natural langage processing systans can comrenimtly utilize factual. knowledge represented in the form of sa~ntic mmrks. The visual suggestiveness of semantic nebmrks aids both&quot; in the forrmrlatian and eypsition 05 the ccmputer data structwes~ they resemble. The use of r;~xnantic mtwrks can b fourd in the wrks of my authors writing on natural language processing (ir~~loding *hank Anderson ard: Bcrwer 1973; ar@ Palm! 1971) as well as other forms of understandhj (Wluding Winstmi 1970; and Guzman 1971).</Paragraph> <Paragraph position="3"> In utilizing semantic network representations, these authDrs have made use of the foll~ characteristics of smtic nets. First (and most important) , nsdes that denote the same cmncept are mt duplimtad (in mst cases). It is then mible that distinct propsitions my impinge on a via arcs. Semrd, propsitions are PSom& by linking predicate ms to their -t nodes usi.rq arcs. Third, since mncepts are rot necessarily word concepts, particular and general concepts are represented as labeled or UnLWed des of a graph.</Paragraph> <Paragraph position="4"> Propsitions my also have des associated with then.</Paragraph> <Paragraph position="5"> Finally, propsitions in a semantic net are rot asslsned to be asserted (@ven though sane researchers treat all nodes as implicitly asserted).</Paragraph> <Paragraph position="6"> The propsition-based -tic nebork mtation of ScM (19745 is especially wdL suited for including pragrmtic and -tic informtion as part of the meaning representation of individual mrd concepts. These mardq representations are netuorks based on ppsitims that consist of an wary predicate with a finite number of agumnts. Terms used in the netmrk to represent a given mrd mpt can also be represented by semantic netmrks.</Paragraph> <Paragraph position="7"> Thus there is rro insisw that a given set of &quot;primitives&quot; form the basis for the nemiq of a W0rd.l The nat section illustrates the use of senantic =rks to represent the ws of word mncqb. Subsequent secthris sketch netbds that involve the -thd. use of these meaning representations in parsmg ard interpreting natural, language text.</Paragraph> <Paragraph position="9"> Cercare (1975) divides kis lexicon Fnto open class itaos and closed class itazls. Typically, closed classes have a strictly limited *ship which cannot be increased by adding new fomaticms or loanmrds (which are mrds that have been bmrpratd by one language fran another language). The significance of closed class itens is beat aqxessed by their gram- function.</Paragraph> <Paragraph position="10"> contrast, open classes have a large, readily increasing rrrmberrihip. New fomtions and loahmrds are easily integra-.</Paragraph> <Paragraph position="11"> Associated with open class category wrds are meanirq representations: one for: each sense of the mrd, The structure of xredng representation is based on the smmtic netmrk notation developed by Sch* (1974). Pragmatic and semantic information are included in the manhg representation.</Paragraph> <Paragraph position="12"> Figures 1 through 6 dmw netmrks that illustrate same of the min senses of the word drink, concentrating on action aspects. For illustrative plrFoses Figures 1, 3 and 6 are divided into a pragmtic section and a semantic section. The pragmatic section indludes the taplate(s) that guides the parse of the utterance and tm lists: the first list contains proposith that represent the implications that are fikely to be needed for the ccmprehensicm of subsequent text; ard the second list contains propositions representing critical implications that we expet xpect mtch in the surface structure. In Figure 1 this first list is (P3) and the second list is (Pl,P2), The mtic section oontains the netwrk that represents the mankg of the mrd sense. Figures 2, 4, and 5 show various dnal senses of the word. &quot;drink&quot;.</Paragraph> <Paragraph position="13"> Notice that Figures 1, 3, and 6 aU have the notion of change in containment location in ccmmn.</Paragraph> <Paragraph position="14"> This rnrre~nds to a general ooncept that subms not only differing senses of &quot;drink&quot; but also other more specific concepts as well, like &quot;eating&quot; or &quot;receiving an enana&quot;. This obsenmtion has led b the follow- consideration.</Paragraph> <Paragraph position="15"> When creating the ming representations (netwrks) for concepts it is desirable to amid the duplication of propositions in storage. If we extractrrore general concepts PSran the specific concepts that they sub- (totally or in part), we can ayoid duplicatian by associating the crmron propositions with the more general concept.</Paragraph> <Paragraph position="16"> In a sense the mrk of both Scharak 11972) and Wilks (1973) wrts the amtention that the mmhg of a mpt is best representd by precatjons at the highest led. of ga-ierality that adequately explain the term's -. Thus we extract frcm &quot;drinking&quot; (and eatbg, etc.) the stmetye shown in Figure 7. We might reasonably label the corcept expressed by this structure - &quot;ingestT1. It is impDrtant to note, Inever, that while Schank and Wilks might mnclude that &quot;ingesting&quot; is a primitive action, that I cansider it a general concept. This applies to all primitive actions prt orw wad of Schank and Wilks. l&amimtion of Figure 7 shms clearly that ingesting is -- mt a primitive action but one wbse meaning is expressed in tens of causes, mtion, time, and other concepts.</Paragraph> <Paragraph position="17"> At this pint the original representations for the various actb senses of &quot;drink&quot;, i.e., Figures 1, -3, and 6, can be replaced with shplified diagrams based on the general concept &quot;ingest&quot;. Figure 8 'shrrrtss the representation of &quot;drink&quot; expressed in Figure 1 redrawn in terms of the general concept &quot;ingest&quot;. In similar fashion Fiv 9 diagrams one meaning of &quot;eating&quot;, again based on the general (mncept &quot;iIlgest&quot;.</Paragraph> <Paragraph position="18"> The key to making effective use of the meaning representation fok ~~=sicm centers on the propssitbns that contain aqummts that we expect to mtch in the surPSace utterance. The lexical itan for &quot;drinkrt wuld contain, amng other things, pointers to a list of pmpsitbns; these propositions contain the argments that we arpect to mtCh with mds in the text ard are mst frcqxx&ly needed for ccmpmhensicn. At times, bwever, other propositions may be required for ampmbmh. For example, the mrd sense illustrated Fn Figure 1 S~-QWS that we expcxk to fM, in an utterance atout drinking, an- anim(x) and a liquid (y) propoisitims P1 ard P2. But the question can be psd, &quot;What is the effect of John's drinking&quot;. To answer this question would entail a PSurther imtestigatim of the other pmpsitions in the ktmrk, especially the first list of implications. Altbugh it is %licit in the senantic structure, ~e make explicit in the pragmatic structure the inference that &quot;x - drink - y&quot; necessarily implies that it causes y's 1.ocation to be - in x at -sane tk after x initiates the drinking action. Of course, she this implication is cxmmn to all senses of &quot;drink&quot; (and eats; inhales, etc. ) it is abstracted into the same general concept &quot;ingest&quot; as well, as sbm in Figure 7.</Paragraph> <Paragraph position="19"> The -tic structure for each wrd sense for &quot;drinks&quot; is represented as properties attach& to the mrd sense. me properties include AW;S, the aqmmt list containing aquments used in the mrd sense; IMPLICS, a list of implications bt acc(npany *the mrd sense; the propsitions P1, P2, etc. that relate the arguments and predicates that make up the netwrk explicating the giveh wrd sense; and tarrplates of the PSom argl arg2 ... argi WRD argi+l ... argn The implications make the mst camonly used inferences part of the meaning representation of word concept. The propositions, for eample P1 and P4 are sb Figure 10. See Cercone (1975) for sarnple laical entries, in particular the entry for &quot;drink&quot;.</Paragraph> <Paragraph position="20"> &my &vantages accrue by represent% meanhg fodas in this way. First, unlike Wilks' (1973) meaning fodas, the representation is suggestive of the meaning of a word. I see m justification for \(binary) lexical decanposition trees as meaning representations for mrds as such trees are neither susqestive of the type of processing required nor of the propositions they encoae.</Paragraph> <Paragraph position="21"> A seoord and mjor advantage is this. The meaning representaf:~ for a word is not required to & explicitly i6 terms of &quot;primitives&quot;. Rather, each of the predicates in the pmpsith that PSom the network repres&ting the meaning of the word can, in tum, be represented in an analogous muma.</Paragraph> <Paragraph position="22"> In particular the notiin of a &quot;cause&quot; seems to me to be m more &quot;primitive1' than &quot;drink&quot;. This metkd of representing wxd meanings enhances the representational schm for the of c~n~rehensb since any munt of detail can be included in the m~aning representations by addhg propositions to the ne-rks.</Paragraph> <Paragraph position="23"> Third, inference ~~, heuristic processing algorithns, and superim psed knmledge-oxpnizing schams can be inaqorated US@ this representation for mrcl meanings as easily as in, any other representation. IMxmplete infomation in surface text can be inferre~3, when necessary, directly frcm the meaning representaeon, in scme cases as a missing argument.</Paragraph> <Paragraph position="24"> The use of this type ~f mdq repressitation for lexical items is further exp1aim3 in the next tw sections.</Paragraph> <Paragraph position="25"> 111. PARSING AND IXlEKP~ION USING NEIFXlRKS ~raditionally, the object of pars- sentences has been to outwt syntactic trees.</Paragraph> <Paragraph position="26"> These trees serwd as inpt to scamtic mutines charged with the generation of mmiq stn&ures. Whgrad (1972) and kbds (1970) tried, wtth sarre degree of suaxss, to integrate the tm processes an3 use each process to guide the other process. shank (1972) and Wilks (1973) have stressed that syntactic processing was secordary to nwning analysis and should be necessary only when the resolution of ambiguity by meaning analysis alone had failed. Utilizing netmrk meaning representatbns the parsing phase is rkmst ccmpletely smtically orient&. QE important q-product m the methcd to be described is the detection of the correct sense of naninals, modifiers and actions.</Paragraph> <Paragraph position="27"> The parsing weeds as follows. Words, in a clause that has been classif3ed2 are i3camed fmn left to right in search of a suitable cardidat& for an action. Once found, the senme is separated into ( (FIRST PAKI?) (ACITON CANDP-</Paragraph> <Paragraph position="29"> of possible action senses that this particular root fom may have. These senses are ordered by a scheme, albeit a very superficial SC~~-E, described in Cermne (1975) . Associated with mrd senses are templates as described above. For example, the sense *GIVE1 of the root form &quot;give&quot; has a taplate &quot;X GIVE Y 2&quot; and an alternative (ALTERN) tanplate &quot;X GIVE Z TO Y&quot; associated with it.</Paragraph> <Paragraph position="30"> The template, e.g.. &quot;X GIVE Y Z&quot; , is used to guide the parsing. In this example XI Y and 2 are variables representing the argUru3nts of the predicate &quot;give&quot; that we expect to find in the surface utterance in the given order. hbre detailed infomatian concerning the argunwtnts is obtained by exmhing the netmrk propositions, for the sense of &quot;givett in question, that involve the arguments. Thus X, in this case, wuld represent ap AKtM?SE rvJnindl capable of &quot;giving&quot;.</Paragraph> <Paragraph position="31"> This is very similar to what Shcank does when parsing in conceptual dependency theory.</Paragraph> <Paragraph position="32"> If the wrds in the surface utterance do rmt satisfy the constraints for arguments of the predicate being examined, it is due to one of four reasons.</Paragraph> <Paragraph position="33"> First, alternab syntactic constructions could exist. Secohd, a different sense of the qctiorr is &quot;correct&quot;. Third, the particular action cadidate in question is not the action of the clause.</Paragraph> <Paragraph position="34"> Finally, sane other reason, like slang expressions might be the cause.</Paragraph> <Paragraph position="35"> Whenever arguments fail to satisfy predicates, a search for alternative inplication templaw begins.</Paragraph> <Paragraph position="36"> The result of this search is shclwn quite clearly J in Figure 11 of Section IV for tbe ternary predicate &quot;give&quot;.</Paragraph> <Paragraph position="37"> In that example &quot;give&quot; is u&d syntactically in two different PSom to distinguish the indirect object, one with the preposition TO and one witbut.</Paragraph> <Paragraph position="38"> If this approach fails then the list of senses for the root PSom is fvther examined.</Paragraph> <Paragraph position="39"> If other senses of the action candidate exist, they are examined further to -e if anymmts of the action candidate in the surface me mtch'variables in the tanplate, TWs pIocedure is repeated until the mrrqct sense of the action candidate is faund or the list of senses is exhausted, If th@ sense list is exhausted, scanning continues in the surface clause for another suitable action Wte and the process is Eepeated.</Paragraph> <Paragraph position="40"> Part of the process of mtchirq argllpeptS of predicates in &ace text to variables in implication tiernplates fnvolves fhxlhg the correct sense of rwminals and Wiers as well. sentace &quot;A drin)rer drinks my drinks&quot; has as the seam? aqmmt of the predicate &quot;drinksn the wrd &quot;drinks&quot;. Possible rwmindl senses for that &quot;drinks&quot; linclde an a1mbli.c beverage, a body of water (thrww John into the drink) , or a thirst quenchere Thus, if the first sense of 'a dndl fails as afgment, other senses mst be examhed before dec- not to accept it as -t. ms rqp&hg applies with respect to nodifiers in a similar kt not identical fashion. For instance, a &quot;yellow cake&quot; is a type or cake like a chocolate cake whereas a &quot;yellow car&quot; is sanething that is ydlow and something that is n car. Using these ~~, sentences such as &quot;A drink^ -er drinks nary drinks&quot; ar8. &quot;The pilot banked his plane near the river bank wer - - -the bank that he an for good bpdcing service&quot; present little difficulty. Mxpblogicdl analysis is ~~t sin& only tbse PSom that tan autplenticaUy be considered as actims need be exmibed. In the example, &quot;A drinker drinks my drinks&quot; the word &quot;drinker&quot; is elimhaW hmdiately as an .action cadidate due to mrphological analysis. Thus, we are very qtlickly able to get a right &ice for an action axtiidate, The next section shows an example of parsing anl the reSulting smtic network con~trtlclt-Rd -- meaning representations of the type described. me - null The following example is taken fmn Cerc~ne (1975).</Paragraph> <Paragraph position="41"> Many other examples can be found we.</Paragraph> <Paragraph position="42"> Tkae mle lis- preceding Figure 11 gives the results of the parsing phase, clause by clause, der the headhg -Ht ASSOCIATED m1m-m</Paragraph> </Section> <Section position="16" start_page="839" end_page="839" type="metho"> <SectionTitle> v. ~SIONS </SectionTitle> <Paragraph position="0"> The above sections outline what I believe to be the wrrect approach to representing the meaning cantent of wrd mncepts. Hopfully the use of mmhg representations such as these will simplify the problens inherat in representing the conceptual amtent of natural language- utterances in terms of meaning structures. In prtiCULarr I see the foll~ desirable features inherent in this approach.</Paragraph> <Paragraph position="1"> (i) Interpretive directness The meaning !structures wrrespw to natural. language utterances are TO& according to s-le structural rules. mful heuristic criteria, based on the central mle of verbs and on preferred -tic catqorieS for the subjects ad objects of verbs, guide Bach choice ih the creation of meaning struchnes. null Interpretation of utterances then takes on a &quot;slot and filler&quot; character, rather than requiring extensive trial and error search.</Paragraph> <Paragraph position="2"> (ii) Wsis of syntax In ordinary discourse it muld, IS absurd not to accept &quot;ungrm&l&quot; -11s like -ling participles or fanciful locations such as mtapbr.</Paragraph> <Paragraph position="3"> In the abwe awch a syneetic straightjacket is mt bps& on assible utterances. Therefore the ahxmnal is mt excluded as it is in my linguistic w'tJ=(iii) -sis an events A mjor part of our interpretative effort in understanding natural language is focused on events, i. e. r tkne-depen3ent relationships. By contrast, &quot;static&quot; ~~ps in the mrld are relatively easy to understand. Therefore the for lkxxlm&tal senantic structures Wuld mncentrate on the representation of events. The use of meaning representatiO11~ as described above facillihtes this emphasis on events.</Paragraph> <Paragraph position="4"> Phe handlira of vagueness, events, the lexical manb-gs of mnp1-m concepts, ard the problem of cwmill knowledge organization may raise additional problans when processing natural language with & representations rmch as the ones I have used. I-Immm, ths meaning repre~entati~ns UW in this paper can be viewed as an extension of several successful but mgerf icially disparate schmata, such as Schank's (1972) conceptualizations Qr Winston's (1970) descriptions. This indicates that their use should prove of real value in the design of understanding Many thanks are due to Dr. Len Schubert; his ideas and amrents are inter- in this research.</Paragraph> <Paragraph position="5"> I am also j,nd&ted to Dr. J. R. Smpson @ Dr. K. V.</Paragraph> <Paragraph position="6"> Wilson for their dful r&iq ar$l suggestions.</Paragraph> <Paragraph position="7"> Notable systans currently in Mgue that utilize &quot;primitives&quot; in this way include tbse of Wilks (1973) ap3 Schank :t al. (1973).</Paragraph> <Paragraph position="8"> 2 Nxds in clauses are rrorpblogically analyzed and, based on that analysis, they are classified to determine all of their pssible syntactic funqtions in an utterance.</Paragraph> <Paragraph position="9"> In WWirogradfs (1972) wrk, &quot;gives&quot; is recognized as a transitive action that requires tw, objects : his classification is TRANS2 .</Paragraph> </Section> <Section position="17" start_page="839" end_page="839" type="metho"> <SectionTitle> #UE ORDERED #NO LABELLED X. 'I. ETC </SectionTitle> <Paragraph position="0"> rigare 1 N-ary Predicate Network -tic retmrks present special problet~~ with respect to the use of logical amectives, quantifiers, descriptions, rmdalities, and certain other constrvctiaas. $Wq&ert (1974) has propased systematic solutions to these p& law by exterding the expressive power of mre or 'less ~omtional samntic netwrk mtation. In this appendix only the elemntary part of the folmalisn, namely only as mch as. is necessary to clarify any yisconceptions than may arise fmn the figures used in this paper, is explainel.</Paragraph> <Paragraph position="1"> In saantic netmrk notation, the distinction between labels designating storage locations and labels designating ~inWs to storage locations requires clarificatbn. This distinction is used & Quillian (1968) to designate &quot;type nodes&quot; (unique storage locatims) verv &quot;token mdes&quot; . The notation can be made uniformly explicit as in Figure A.1. Here &quot;part-of&quot;, which in sane rotations corresponds to a token node, designates a type node (as suggested b$ Winston, 1970). All mcircled nodes correspond to storage locations andmall arrows to addresses of storage locations. What fozm~ly were token nodes are mw called--sition nodes; they serve as graphical nuclei for propositions as a wble.</Paragraph> <Paragraph position="2"> At times the explicit natation- of Figure A.1 will clutter tho diagram leading to a loss in readability. Wefore, when the meaning is cle, binary predicates will be represented as in Figure A.2 for visual effect with the understanding that the use of explicit propositidns underlie the structure.</Paragraph> <Paragraph position="3"> In Figure A.1, A, B, and REL are mere distinguishipg mks. They are analogous to parenthesis or ccmas in the Predicate Calculus & that they serve to relate demthg terms syntactically; they are mn-demtative thenselves.</Paragraph> <Paragraph position="4"> Whenever possible they will be cbsen to be =-, i.e. to enhance readability and be suggestive, but they amid be chosen as numeric labels as well.</Paragraph> <Paragraph position="5"> One adwhtage of the explicit.notation of Figure A.1 is that it works for n-aq (1172) predicates.</Paragraph> <Paragraph position="6"> The sentence &quot;John gives the hk to Mzty&quot; involves &quot;gives&quot; as a three place prediate. * It is diagram& as in Figure A.</Paragraph> <Paragraph position="7"> Figure A.3 is appealing because of the significance we can attach to labels agent, object, and recipient. 3y no means is Figure A.3 a graphical analogue of &quot;case-skructured&quot; grmmars.</Paragraph> <Paragraph position="8"> Cases are not view& as conceptually primitive binary relations as Filhre (1968) and res~ar~hers influenced by him, notably Schank (1972), view than. In a case structured system the central ncde would denote a specific action or process with the property that it is a &quot;giving&quot; and involves John, the book, and bkzy as agent, object, and Pecipient respectively. Case relations can be understood as ccmplex mnprimitive terms derived fm such causally and telmlcgically related sequences of states. The wble notion of a case derives frcm the syntactic and sa~ntic similarities bebeen the role played by the argurrrents of many predicates. Nevertheless the mtion of an &quot;agent&quot; to depend in part on causal priority of a state of the supposed agent in the sequence of states mer consideration, and in part on the extent to which purpsive behaviour can be ascribed to the supposed agent in general, and in part to the extent to which the particular sequence of states which he initiated can be assum3 to be intentional on his part. See Cercone and S&ndxrt (1974) for a further discussion of cases.</Paragraph> <Paragraph position="9"> One f inaL notational pint by way of introduction needs to be made. The &quot;camn labels in Fim A.3 are to be med as me mnem~cs, altbugh indicative of mre canplex relations. To avoid confusion, predicate names will be designated &quot;in mall letters and markers by capitals.</Paragraph> <Paragraph position="10"> Other conventions that are used-include: solid loop for propositional nodes and existentially quantified amcept nodes; bmken lwp for universally quantified concept nodes; solid lines to the parts of a propsition to a propsition node; dotted lines for dependency links joining each existentially quantifiedmde to all universally -+.ifid des on which it depends; and broken Lines for logical I-.</Paragraph> </Section> class="xml-element"></Paper>