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<?xml version="1.0" standalone="yes"?> <Paper uid="J79-1018"> <Title>Hendrix, G., rel lid nary Consfructs $or the Mathematical Modelling of English Meanings &quot; Univers$ty of Texas, ~e~arthent of computer Sciences, Working Draft, April 1974. (not for distribution) I I Hendrix, C.G., Thompson, Craig and Slocum, Jonathan.- Language Processing It</Title> <Section position="1" start_page="0" end_page="0" type="metho"> <SectionTitle> SEMANTICALLY A~*ALY~I G AN ENGLISH SUBSET FOR THE CLOWliS MICROWORLD Rob.ext F. Simo'ns </SectionTitle> <Paragraph position="0"/> </Section> <Section position="2" start_page="0" end_page="0" type="metho"> <SectionTitle> ABSTRACT </SectionTitle> <Paragraph position="0"> A microworld system is described for displaying visual representations of the meaning of a subset of Eng,lish thak.concerns a clown that can balance objecbs and can participate in motion scenarios. Nouns such as &quot;clown&quot;, &quot;lighthouse&quot;, &quot;water&quot; etc. are programs that construct images on a display screen.</Paragraph> <Paragraph position="1"> Other nouns such as &quot;top&quot;, &quot;edge&quot;, &quot;side&quot;, etc, are defined as fm~tions that return contact p~ints for the pictures.</Paragraph> <Paragraph position="2"> Adjectives and ad rerbs provide data on size and angles of support. Prepositions and verbs are defined as semantic functions that explicate spatial relations among noun ifnages. Generally, a verb praduces a process model that encodes a,series oftscenes that represent initial, intermediate and final displays of the changes the verb describes.</Paragraph> <Paragraph position="3"> The system is programmed in UT.LISP fqr CDC equipment and uses an IMLAC display system. It] currently occupies 3210K words of core and requires less than a second to translate a sentence into a picture. Applications,to teaching linguistics and languages are suggested.</Paragraph> </Section> <Section position="3" start_page="0" end_page="0" type="metho"> <SectionTitle> ACKNOWLEDGMENTS- </SectionTitle> <Paragraph position="0"> This. resear'.l was supported in part by NSF Grant GJ509E.</Paragraph> <Paragraph position="1"> I am indebted 4ia Bill Henfieman, Jonathan Slocurn. Michael K.</Paragraph> <Paragraph position="2"> Smi-th, Ken Speaker and Bob Amsler for productive discussians and help ih designirig and debugging the programs described here My thanks to Professor Woodrow Bledsoe for making available the IMLAC- display gnd its operating systems.</Paragraph> </Section> <Section position="4" start_page="0" end_page="0" type="metho"> <SectionTitle> ,NATU~W LANGUAGE ESEARCH FOR CAI </SectionTitle> <Paragraph position="0"/> </Section> <Section position="5" start_page="0" end_page="6" type="metho"> <SectionTitle> SEMANTICALLY ANALYZING AN ENGLISlI SUBSET FOR THE CLOWNS mCROWORLD I Int~oduction </SectionTitle> <Paragraph position="0"> Several examples of semantically based grammars have appeared in the literature since 1970.</Paragraph> <Paragraph position="1"> The most corttplete of these are wknogradls (1972') outline of a systemic grammar for commanding and questioning the robot hand in the MIT blocks world, ~eidorn' s (1972). rewrite rules for anal'yzing and generating ~nglish descr'iptions and transforming them into GPSS programs, and 'the ATN gramdar of questions for the*Lunar Rocks Data Base presented by Woods, Kaplan and Nash-Webber (1972). Most other grammars of significan size, such as that of the NYU String Analysis Project (Grishman and Sager 1973) and Werous gramrs developed for mechanical translation are largely syntactic in orientation and not easily accessible. Riesbeck also presents a semantic grammar m rne form of a sek of LISP programs to cornput conceptual dependencies (1975).</Paragraph> <Paragraph position="2"> A difficulty with these reports is that th6 systems using the grammars are typically+ quite large programs--loOK+--and the interactions between the grammar and the rest of the system are frequently quite complicated. The reader who wishes to use them as a basis for constructing small natural language understanding system may well be at-a loss as how to-begin. He may have the impression that a natural language pro~essing~aystem is a vast undertaking involving great complexity of programming.</Paragraph> <Paragraph position="3"> He will not be co'mpletely incorrect in these impres.sione, byt in fact p-ramming a grammar and. aemantic system fot a micrdworld model to underetand a small subaet of English is no lonuer a formidable task. khe vocabulary can 'be restricted to one hundred or so words, a minimally sufficient syntactic and semantic syatem can be expremed in a few dozen rules supported by a dozen or so aemantic functions, and the pragmatics of guch microworlds as the STRIPS robot, the blocks world, or the CLOWNS world presented here, can be modelled very stmply, The siqplest microworld models that comunicete in English require an effort somewhere between a two week homewodc exercise and a graduate term project. CLOWNS represents about 6 man-months of effort so far.</Paragraph> <Paragraph position="4"> But is there any real purpose in studying English communication in these trivial microworld situations? If we mbdel language behavior in one microworld we remain eeveral orders of magnitude short of understanding the genera2 use of the langu~ge in text, or in verbal discourse and equally far from the possible g~al of tnstructing computers In English to accomplish a general run of tasks.</Paragraph> <Paragraph position="5"> I: remain Incurably optimistic. The generalizations about tiny subeets of language and b+avior that emerge from microworld models gradually accumulate in bur human minda into what may eventually prove sufficient understanding fiv the accompliehment of socrally useful tasks. me initiation rltual of programing a mini-intelligence is a r)ecessary pre-requisite to programming one that is more sophiaticafed.</Paragraph> <Paragraph position="6"> In this paper, CLQWNS, a simple microworld model is presented with an explicit tutorial intent. A brief grammar is described that accounts for much of the embedding logic of English canstructions; a flystem of transformations of Eng1iah.condtituents to property list representations of semantic network structures is followed by their represen,tation in a dynamic process model that can be operated to produce successive states deecribed by the English.</Paragraph> <Paragraph position="7"> The principles used in the system are.a concise representation of my gleanings from recent literature dnd of course from work of my own and my students.</Paragraph> </Section> <Section position="6" start_page="6" end_page="9" type="metho"> <SectionTitle> 11 Background </SectionTitle> <Paragraph position="0"> In this section only a few of hundreds o'f natural language processing papers are suggested as entries to thelliterature. At least a dozen reviews of this liteyature are available; halker's is not only among the most recent and complete (Walker 1973), but it includes a section that cites the reviews.</Paragraph> <Paragraph position="1"> Since 1970, the langu'age processing literature has been rich in reports of natural langbage systems that can understand subspts of English with respect to various microworlds. In addition to previously mentioned work by Woods, Heidorn and Winoppad, there are less frequently cited but quite interesting theses by Badre (19.72) That learns to do very simple number problems from text, by Scragg (19-75) that answers questions about food preparation processes and by Bruce (1972) that presents a logic and a system for answering questions about temporal reference. Schank Riesbeck, Goldman and Rieger (1975) have publisheda significant series of papers on Semantic parsing., inference and generation for an Endish subset concerning fairly ordinary human action$. Hendrix, SLocum and Thompson (1973) describe* a systexh for under.s-ding and generating English about commercial transactions and Mmple movements.</Paragraph> <Paragraph position="2"> Hendrift (1975) has also developed a set theoretic system of proteos models for representing natural language meanings. TheSp models are descended from robot problem solvidg research by Fikes and Nilsson (1971) and siki6ssy et. al. (19i3). Harris (1972) provides a tour de force that uses problem solving, inference agd learning methode to teach a robot facts about its microworld. Hobbs (1974) presenta an hpproach to natural language semantics that ig shown to apply to several applications, diagramsto-language,, English and Algol-to-Algol, e tc.</Paragraph> <Paragraph position="3"> Much of the most recent work by Abelson (1975) , Charniak (1972 ) ,' Schank and Abelaon (1975), .Mineky (1975), Winograd (1975), Bobrow and Norman (1975), Collins and Warnock (1975), Rumelhart (1975) has progressed beyond the question of grammar and semantic systems to that of such larger units of semantic organization as Frameg, Stpry grammars, Plane, Schemes, Drems, etc. Although at this writing most of these formulations still fall short of computational realization, it is clear that the research task of the immediate future is one of formulating and programing structures of organizationrthat will successfully model much more complicated microw~rlds than those presently achieved. A forthcoming book edited by Collins and Bobrow will present many of these ideas.</Paragraph> <Paragraph position="4"> LISP is still the language of most frequent choice for these experiments and thanks to the prevalence of virtual memories and virtual LISP,)the limitation to inrcore implementations has essentially vaniehed.</Paragraph> <Paragraph position="5"> Many of the programs cited used require from 100 to 300K10 cell8 of smrage.</Paragraph> <Paragraph position="6"> l'he system described in subsequent .eections resides in 32K on a CDC syegern, although our moat recent additions have caused ue to use e virtual mbry version of UTLI$P that was developed by -Wry Tyson.</Paragraph> </Section> <Section position="7" start_page="9" end_page="9" type="metho"> <SectionTitle> 111 Pictor-ial Models </SectionTitle> <Paragraph position="0"> Ignoring early w~rk largely lost in the archives of corporate memos, Winograd's language processor is essentialky a first reporting of how to map Englpsh sentences into diagrammatic pictures. Apart from potential applications, the pictures are of great valve in providing a universally understood snecond language to demonstrate the system's interpretation of the English input, While we are still struggling in early stages of how to compute from English descriptions or instructions, there is much to be gained from studying the subset of English that is picturable. Translation of English into other more general languages such as predicate calculus, LTSP, Ruseian, Basic English, Chinese, etc. can provm the same feedback as to the system's interpretation and must suffice for the unpicturable set of English. But for teaching purposes, computing pictures from language is an excellent instrument.</Paragraph> <Paragraph position="1"> We began with the notion that it should be quire easy to construct a microwcizld concerning-a clown, a pedestal, and a pole. The resulting system cauld draw pittures for such sentences as: A clown holding a pole balance6 on his head in a boat.</Paragraph> <Paragraph position="2"> A clown on his arm on a pedestal bglances a call clown or his head.</Paragraph> <Paragraph position="3"> Figure 1 shows examples of diagsamg produced in responBe 'to these sentences.</Paragraph> <Paragraph position="4"> Ue $roereesed fhen to smtcnces concerning movement by adding land, water, o lighthouse, a dock and aboat. We were then able to draw pictures such as Figure 2 to represent the meanings of: A cloh on his head eaile a boat from the duck to the lighthouse.</Paragraph> <Paragraph position="5"> In the context of graphics, two dimensional line drawings ate attractive in their 3implicfty of computation.</Paragraph> <Paragraph position="6"> An obj~ct is defined as a LOGO graphzcs program that draws it (see Section VI) A scene is a set of ob~ects related in terms of contact points, A scene can be described by a set of pradicstes</Paragraph> </Section> <Section position="8" start_page="9" end_page="14" type="metho"> <SectionTitle> (BOAT ABOVE WATER) (ATTACH BOAT* WATEqpl) (~CK ABOVE WATER) (DOCK LEFTOF WATER) (BOAT RIGHTOF WK) (ATTACH DOCYky WATE%) (ATTACH BOATXl+kY DOCSy </SectionTitle> <Paragraph position="0"> Orientation functions for adjusting starting points and headings of the programs that draw the ob~ects are requlred and these imply some trigonornetrlc functians A LISP package of about 650 llnes has been developed by Gordon Bennett m p~ovide the plcture making capablllty What 1s rnalnly relevant,to the computation of language meanings 1s that a semantlc structure sufficient to transmlt data to the drawing package is easlly represented as a property list associated ulth an artlficlal pme for the scene For example, A CLOWN ON A-PEDESTAL&quot; results in the following stbqture (Cl, TOK CLOWN, SUPPORTBY C2, ATTACH(C1 FEET= C2 TOPXY)) (EU2, TOK PEDESTAL, SUPPORT C1, ATTACH(C2 TOPXY Cl FEETXI)) (CLOUN, EXPRCWDAO ,) FEET XI, SIZE 3, STARTPT XY, HEADING A) (PEDES~AL. EXPR(LIU.IBDA() ) TOP XY, SIZE 3. STARTPT XY, BEADING A) A larger scene has more objects mare attach relations, and may ~nclude addltlona2 relations such as INSIDE, LEFTOF, RTGHTOF, etc In any case the scene is, s'eaantlc+lly represented as a set of objects connected by relations in a graph (1 e a semantlc network) that can easllj be stored as a property list wlth references to other objects with property lists l r We take &quot;balance&quot; stand' support &quot;hold ' is on&quot; etc. as state describing verbs in contrast to those such as &quot;sail&quot;, ' ridef , fly' &quot;buy etc whlch descrlbe changes of state To model the meaning of state verbs requlres only a single diagram to show the state described Far change of state verbs a serles of plctures'is required and a process model IS used to construct a sequence of state descrlptlons each of vhlck can produce a diagram IV An Engllsh Subset Grammar We take the Woods ATN as a baslc formalism f~r describing a grammar computationally This system has been well-described by Woods (19M), lts application to Engllsh semantics by Simmons (1973) and a UTLISP version was programed by Matousek & Slocum (1972) Whlle generally ignoring theoretical lssues In linguistics, we do use such principles as the fact that sentences are composed of constituents, that there are syntactx rules definlng acceptable sequences of constibuents. and that.underlying the Engllsh statrment there is an idea that can be expressed in Sume other language by transformations on the Engllsh constituents The underlying idea can be expressed in a formal language such as some version of predicate logic, or in a computer data structure 01 in a langr~age of fumtlons and arguments such In presenting the.EUollowinp grammar and semantlc system our emphasis ~s on dealing with the highly dariable nature of Fnglish embeddings This means that we have bean more interested in the many forms of dependent clau~e--~re~ositiondl phrase, relative clause, inf lnitive, participial phrase, relative cpnjuetive clause, etc,--than in the ffne detail on noun phrase, noun-noun combinations; and the fine grain of verb sttings We have also for the moment ignored ordinary conjunctions in view of the clear treatment offered by Woods, Wipograd and Grishman; each of whom points out that an and or an &quot;~r&quot; triggers a special subgrammar that attempts to find a structural repetition of a constituent that was just cbmpleted. Bqcause of our interest in embeddings we have chosen to consider relative clauses at the toplevel of the grammar where possible.</Paragraph> <Paragraph position="1"> The following constituent description defines a very fluid subset of English with great potential for embeddings.</Paragraph> <Paragraph position="3"> 'VINF -+ TO SUPPORT, . , .</Paragraph> <Paragraph position="4"> VPRESPART +.SUPPORTING, SAILING,.. RELPRON -+ WHO, WHICH,, WHAT THAT RGONJ -, BEFORE, AFTER WHILE AUX -+ IS WAS; HAS, HAVE, Hw ADV + HDRIZONTALLY. VERTICALLY V ' SUPPORT $ALAPJCE, SAIL PRON + F@, SHk, ET , THEY ., ART + A, AH, THE ...</Paragraph> <Paragraph position="5"> AD5 -+ LARGE, SMALL, TINT' 1N + 'CLOWN. 'PEDESTAL, BOAT, DOCK,'FEET, TOP, SIDE .., In the a'bove: + means &quot;followed by7, (x) means optional x. x* pedns 1 \I or more means &quot;or&quot;, . . . means etc. ,. md x/y means x is the lnitia &quot;element of p. The arrow.+ means &quot;defined by:'. The form of notation above-is a concise recurgive description fcr the ordering of constituents. It shows nothing abqt the semantics that may be included in the sptem, and the flow of control far parsing is not at all wbvious. Augmented Transition Netwcwk.graghs foLlowing Woods show the conditions,on elements of the sentence and the flow of control in terms of directed arcs leavlng*nodes in a two-dimensisned diagram of the grammar Even more irnpartantly, an ATN proviaes f~r the display of semantic operations that are to be undertaken on each const2tuent. The convention for drawing an ATN is to write conditional statements above the arcs, and operations below.</Paragraph> <Paragraph position="6"> For example :</Paragraph> <Paragraph position="8"> In this net, if the sentence begins with an NP, the PUSH NP will return Ehe structurk of an NP iq the * registei. At that point the registex SUBJect is set to 'that value. When a VString is analyzed. by PUSH VS 'then V is set to the value'VS returned. At this point further structure is bvllr-by PUTting on the verb's property lisk the attribute SUBJ with the value contained $n the register SUBJ. Similarly, when an OBJect NP is parsed, it can be added to the structure of V and,the value of S can be POPped-4.e. returned--as the register V>which will allow access to the property list of rhe verb on which the values of subject and object can be found by consulting those properties as in (GET (LAST(GETR V)) &quot;SUBJ) . The function LAST is used in thi's exhmple to obtain the last element of a list.</Paragraph> <Paragraph position="9"> Notice this e~ample illustrates.that our general approach to recording vsemantic information is one of putting detailed information such as the arguments or cases of a verb on the property list of that object. Thus the result of parsing &quot;clowns hold, poles&quot; with the above net is:</Paragraph> </Section> <Section position="9" start_page="14" end_page="23" type="metho"> <SectionTitle> (HOLD SUBJ CLOWNS, OBJ POJ+ES) </SectionTitle> <Paragraph position="0"> In,fact, it is necessary to create new names for each word used in a sentence--to avoid clobbering dictionary information--so the result from actual nets would be:</Paragraph> <Paragraph position="2"> Tke:- relation TOK shows that C1 is, an instantiation of'-the lexiix.11 item HOLD. In this convention for stating property list values, the first element is the ATOM and each pair separated by comas IS an ATTRIBUTE and i-ts VALUE.</Paragraph> <Paragraph position="3"> The Wdods system also btores it$ pa-st states and provides for backup in the event that no conditional arc succeeds and yet there is still sentence to be scanned., In this event the system recursively consults the state leading to the current node'to see-if there were arcs that wer.e untried that lead to a successful parsing for the sentence string.</Paragraph> <Paragraph position="4"> T-he * register has special sigdficance in that ordinarily it contains the sentence element under the scanner, except wheo a subnet such as NP returns a value, in vhidh case the POP arc sets the value in the * register. The overall flow of control through an ATN is that * is set to the first element of the sentence, then the topmost net, CLAUSE ot S, applies the grammar in topdown fashion. Each the a constituent --a word, a'phrase, a clause--is recognized and control is passed to another notie, the scanner' is advanced and parsing proceeds from the new node. For programing simple grammarg without much embedding and without backup capabilities a~1 ATW may be used a8 a flow chart to design the program.</Paragraph> <Paragraph position="5"> If more complex grammars are requhed, Woods has provided a complete set of language conventions -and gn Interpreter with the capability of storfqg past states and backup.</Paragraph> <Paragraph position="6"> hxicdn t English wpsds; their lord claerres ad f eaturea and other information such ae program definitioos etc. are recorded on a property list structure for eady access by fun&tions used in the ATN. The follow ing examplee illustrate this structure:</Paragraph> <Paragraph position="8"> The fuaction (PUT X Y 2)--e.g. (PUT &quot;CLOWN &quot;NBR ''SING)-YI~~ add the pair (Y 2) to the atom X or replace the value of X's attribute Y with the new value Z. The function (GET X P) will then return the value Z. Such ATN functione as CAT ahd GETP simply call GET tdth the first argument set to the value of the word under the sentence scanner.</Paragraph> <Paragraph position="9"> The l$XPR yalues associ'ated with an Engaish word are aedtic functions that are explained later. by modificaticms to this simpjrr? spheme can be aaded to provide for morphorogical variants referring-to root f om instead> of rhquiring a. definition oi-thef r own, and an attribute; POUOWEDBY, can be used to cbllact multiple ward terms. The basic property list representation of a dictionary can be expanded to include multiple word senees as well, but it always re,tains the character aE a basic LISP syetem for storage and retrieval of data aesaciated with an atom,</Paragraph> <Paragraph position="11"> Gltammar: This is thk toplkvel net for the grammar. I$ is named clause and transfers control to states C1 and C2 each of which can POP a value in the event that the sentence string has been completed or a clause successfully paased. The barred pointer,* , -indicates a HOP, operation which passes control without advancing the sentence scanher or changing the * register.</Paragraph> <Paragraph position="12"> This net accepts sentences beginning with an NP, a VP or a dependent clause. [.ID is the name of a register that generally contains the last constituent found. The UNHOLD arc emanating from C1 causes a list, HOLD, to be processed. HOLD contains Dependent Clauses that are missing soare element that delays their semantic processing.</Paragraph> <Paragraph position="13"> Phx example, &quot;on his nose&quot; in &quot;on hie nose a clown balances&quot; caanot be eema@,~fcally procesped until ll~lovnl' ehovs up as a following NP.</Paragraph> <Paragraph position="14"> The net ie satisfied by a sentence or-by a single noun phrase euch ae a clown in n boat&quot; dr by an imperative, &quot;balance a pedestal&quot;. It ddee not ac'cept queetion forms; that would require an additional arc from CLAUSE labelled, PUSH QFOBN SNTC.</Paragraph> <Paragraph position="15"> The ordinary form of an arp is an arc-label such as CATegory, PUSH,, POP, TST followed by its arguL ment, followed by any-condition statement.</Paragraph> <Paragraph position="16"> SNTC is simply the variable that contains any remaining sentence string, so the condition SNTC is true except when thelstring has been..exhausted. If SNTC is nil, there is no point in further processing.</Paragraph> <Paragraph position="17"> The arc PUSH W (SENDR sUBJ)~will send the value of the register SUBJ to the qubnet W.* If VP is SUCC~SS~~~, the operation under the qrc (EVAL-((GET * TOK) * )) will caJ1 for a function associated with the verb to translate the subject, ~bject and complements of the sentence into the particular semantics of pictorial relations. The verbs SUPPORT, SAIL, and MOVE are defined as semantic functions in seetion V. PreA positions are also defined as semantic functions in that section. When HD is popped from C1 or C2 it contains the name of an object ondthe property list as described earlier. The resblt of a parse is an atom name whose property list contains labelled references to its arguments which are either symbolic or numeric values, or references to other atoms which have property lists. ~h&quot;is of course is a property list representation of a semantic network.</Paragraph> <Paragraph position="18"> This NP net is operated dh the tall, PUSR NP T.</Paragraph> <Paragraph position="19"> It allows for a prono--or a sequence of (arr)(adj*) N. Its operation intludes some basic semantic transformations on the head nbun. If the sentence begins with an-ARTtcle, the determination is set to DEPINITE or INDEFINITE depending on what feature GETF finds associated wdth it. A pronoun implies definite determination, and a noun phrase withouban article implies indefinite exceptdn the case of proper nouns not considered in this net, Adje'ctives are appended to a list named MOD.</Paragraph> <Paragraph position="20"> When the noun head is encountered, MAKETOK creates an atomic name ~i using the LISP function (GENSYM C) and puts on its property list, the pair, TOK WORD. The remaining operations under the CAT N arc add property value pairs to this TOKen of the noun. From NP2 the atc, POP HD (PUTMODS HD), is encountered. PUTMODS is a semantic function that works with adjectives and adverbs iri the following fashioe: An adjective, e.g. big, has the following lexical structure: (BIG ADJ T, POS T, TYPE SIZE, VALUE 7) PUTMODS will for each adjective obtain the TYPE and VALUE and put them on the noun's property list. Thus, &quot;a big red clown&quot; results in: (C1 TOK CLOWN, DET INDEF, NBR SING, SIZE 7, COLOR 1) where COLOR 1 assumes that some mechanism for assigning colors likes numbers as inputs, even as the drawing programs require numerical values for 3IZE. The result of parsing a noun phrase with thismetwork is to return the semantic structure of an object as a set of property-value pairs associated with the name Ci which is a tbken of the word used. The net is not sophisticated as NP definitions go, much more complete grammars of the NP are offered by Winograd and Woods.</Paragraph> <Paragraph position="21"> The lack of a continuation into a modifying clause such as a PP ordrelaYive clause is deliberate in that we prefer to rbturn control to the structure calling the NP so that its syntacticsemantlr position in the higher sequence can be used by the Dependent Clause net.</Paragraph> <Paragraph position="22"> discussion, but it scans the sentence string for an acceptable sequence of auxilaries, and adverbs domindt'ed by a verb. It makes a token of the verb and puts its tense and auxidiaries on that token as property value pairs. It returns the token name. In exiting node VP1 we seek an NP as a syntactic OBJect and finding one, add the subjgct and object as properties of theverb. If no NP folloys the verb, the next arc tests to debermine whether the verb is a passive form and if so sets the flag PASV, sets object to subject, and subject to nil. If a &quot;by&quot; prepositional phrase follows, it becomes the subject. Additional modifying phrases are picked up by the DCUUSE loop. No actlons are associated with PUSH DCLAUSE arcs because each DCLAUSE calls semanti,~ routines that bind the modifier to the noun or verb it modifies--frequently not the one it ilnmediately follows * The YP net accepts a verb, a verb group, or a verb group followed by an NP and a string of PPs or other modifying clauses.</Paragraph> <Paragraph position="23"> It lacks the case of two NPs to account for direct and indirect objects.</Paragraph> <Paragraph position="24"> The DCLAuSE.net is fairly intricate in that it accounts for PPs, relative pronouh clauses, infinitive modifiers, participial c Wses and clauses introduced by relative conjuncti~ns. A PP is we or more prepositiotrs followed by an NP. A RELCONJ starts with an RCONJ such as &quot;while&quot;, &quot;after1' etc. and may be followed by a DCLAUSE or a CLAUSE.</Paragraph> <Paragraph position="25"> A relative, pronoun clause begins with an optional relative pronoun and is followed by a pronoun clause which is either a VP or an NP fohlowed by a VG an6 optional DCLAUSES. For the moment we insist for computational economy that a relative clause he introduced by a relative pronoun; actually the fm of a pronominal clause is sufficiently rwell defined that PUSH PRONCLAUSE can identify it without a relative pronoun -in most cases.</Paragraph> <Paragraph position="26"> When a pronoun is found, here or in an NP, the- function ANTECedent is called to scan the list of prece'ding nouns, to find the best agreement-in person, number, and gender. The function VBMATCA on the exit from node D2 is a function that seeks to find the head that the participial or infinitive phrase is modifying.</Paragraph> <Paragraph position="27"> As in PREPMATCH, the head noun is frequently not the one just preceding the modifying phrase and-the particular verb and its ending are usedl ip choosing its head nauh or verb. GLST is the name of a list of candidates.</Paragraph> <Paragraph position="28"> In 'the event that the DCLAUSE is a relative pronoun or a participial or infinitive construction, the final step is to call the semntic function associated with the verb and evaluate it for the subject, object and complemeht arguments. DCLAUSE is undefined for adjectival and adverbial clauses that can be used~as modifiers. When defined they can be added as additional arcs.</Paragraph> <Paragraph position="30"> This abbreviated PP net is presented to call attenti~n to its method for accepting a ~tring of prepositions and for accomplishing the semantics by calling PREPMATCH. Although Section IV concerns semantics, it is warth noting that the eftect of PREPMATCH is to add information to the semantic structure reptesenting a noun or a verb. For exa~ples: &quot;a clown on a pedestal on his nose</Paragraph> <Paragraph position="32"> ... balances on a pedestal on his nose&quot; (C2 TOK BALANCE, TENSE PRESENT, COMPS (IPE~ESTAL #NOSE) ) Thus if a verb intervenes beween a voun and prepositional phrases that might ntodify it, the PPs become COMPlements to the verb under the attribute COMPS, and the verb's semantic function has the task of relating it to other elernems of the sehtence.</Paragraph> <Paragraph position="33"> V Semantics of the Subset Parsing a sentence with the ATN grgmmar just described results in a get of symbols edch of which is further characterized by attribute* and values on a property list. If no semantfc functions were applied--such as those associated with prepositiwns, modifiers and verbs--the result would be a tree such as the following:</Paragraph> <Paragraph position="35"> which is minimally sufficient information for the graphics to produce a single icture to represent the state of affairs the .sentence described.</Paragraph> <Paragraph position="36"> It s perfectly feaslble to compute the syntactic form first and then apply the semantics, but as Winograd, Riesbgck andmothers have found, the early application of semantics can be used to minimize the ambiguities of the syntax. For this reason, as each prepositidnal phrase is parsed a semantic function is called to determine which noun or verb might be its governor or head. Each time a Verb Phrase is completed, a sema~tic function is called to translate its syrltactic arguments, i.e. SUBJ, OBJ, COWS, into pictorial relations such as SUPPORT, ATTACH points, etc.</Paragraph> <Paragraph position="37"> Semantics of Prepositiops: After a PP constituent has been identified, a function PREPMATCH is called with a list of the nouns and verbs so far encountered, GLST.! Each preposlition is associated with a function that examines a candidate head from GLST-hnd the naun object to decerpine if the candidate can dominate the PF in question. For example &quot;ON1' is defihed as a LISP function with two arguments. When called with '&quot;clown'! and &quot;nose&quot;, ON returns a structure in which the ATTACH poifit of the clom i's the XY coordinates of his nose. When called with &quot;clown&quot; and1&quot;pedestal&quot; it returns a structure in which the pedestal SUPPORTS the clown. If dallad with &quot;nose&quot; and &quot;pedestal&quot; it returns NIL sihce nose is neither- ah independent picturable object nor a paft of the pedestal.</Paragraph> <Paragraph position="38"> PREPMATCH does the book-keeping by calling the preposition function with each candidate from the GLST, If*he candidate is a verb tlha~ can be modified by that preposition, PREPMATCH adds the PP to the verb's list: of'C0@S, and the verb gemantic function will interpret it. Tlie function BESIDE offers a simple example definition that shows how dne prepositien can imply another.</Paragraph> <Paragraph position="40"> Thue. &quot;a beside b&quot; is quite arbitrarily interpret& to mean &quot;b ie ~o the right of a&quot;.</Paragraph> <Paragraph position="41"> RIGHTOF requires that its two arguments be picturable objects.</Paragraph> <Paragraph position="42"> &quot;A clown on his nose beside a pedestalr' causes PREPMATCH ((NOSE, CLOWN) PEDESTAL). PREPMATCH first-calls (BESIDE NOSE PEDESTAL)</Paragraph> <Paragraph position="44"> PICTure. Then PREPMATCH calls (BESIDE CLOWN -PEDESTAL) and the return is (essentially*) PEDESTAL RIGHTOF CL,OWN.</Paragraph> <Paragraph position="45"> Somewhere else in the forest, the relation RIGHTOF will be interpreted to mean contact between leftsi.de and'rightside of two objects. So we.quite arbitrarily' force a.p.lte~ise meaning--so far suff&ci~rit for our purpose-om the geometrically vague term, &quot;beside&quot;. In general the prepositional semantics for a micrpworld model are definable where the number of possible meanings for each preposition are limited by the situation. ~h the CLOWNS wor Id, &quot;with&quot; &quot;on&quot; and &quot;by&quot; have multiple meanings that are selected .in accordance with the conditions described by their semantic functions.</Paragraph> <Paragraph position="46"> In .contrast, &quot;from&quot; so far has a single meaning.</Paragraph> <Paragraph position="47"> Verb Semantics: The English verb is a remarkably complex conceptual object. It may carry several aeanings dependent on its arguments and on its larger con-text. It communicates information about temporal ordering of its process by auxiliaries and its suffix. It implies one or a sequefitial seriee of events. Its syntactic positi6n and ending can be used to signal that it is a pre-modifier or a post-modifier for another verb or a noun.</Paragraph> <Paragraph position="48"> It is part of a clasqification structure and may imply special argument valuee to some more general verb higher in the classification. Far example.</Paragraph> <Paragraph position="49"> * Where these examples use words trhe functions are using Ci tokens or words appropriate.</Paragraph> <Paragraph position="50"> 2 8 &quot;retort&quot; means '.'answer sharply&quot; ~hich means &quot;comutlicate sharply ii~ response to a communi.cationt'. The verB mdy imply Gpecial arguments in another way; the verb, &quot;sail&quot;, implies that &quot;someone caused a vehicle ta move through a fluid by a means involving aerodynamics from 'one place to another&quot; If the sentence omits some of these arguments, the verb semantics implies them. Thus we can sail a boat, a kite, an airplane, a saucer, but hardly a locomotive or a desk. If the arguments are idappropriate we can ascend the classification tree and call the statement a metaphor. In addition, the verb allows its arguments to occupy practically any syntactic position in the clause or sentence and must sort theui out oa the basis of semantic informat ion.</Paragraph> <Paragraph position="51"> By analogy, a verb is a dramatic skit with a variable set of characters that successively relates the character roles to one another over a period of tjme. A verb has a set of a'rguments, case roles filled by semantic objects; it has an initial state, a set of relations among its characters; a set of intermediate states, one or more sets of relations among its characters; and a final or resulting,,state similarly charafterized. In addition, receht work particularly by Abelson and Schank suggest that in a given culture a verb models a situation that is predictably preceded and followed by rntrre or less typical situations. If a person strikes another person,,the first one was probably angered by the second, dominates the second, etc.</Paragraph> <Paragraph position="52"> while the second, feels pain, map react in anger, etc. So it is reasonable to scppose that our experience is organized in scripts, frames, scenes, dremes, etc. whose component elements include the dynamic skits that verbs signify In the CLOWNS world a verb selects an associated semantic function to sort its arguments into typical roles in its picturable dr.amatXc skit and relates them in typical ways for display as initial, intermediate and Final conditions.</Paragraph> <Paragraph position="53"> In this rashion, the verb &quot;sail&quot; relqtes an Agent, a Vehicle.</Paragraph> <Paragraph position="54"> a Medium, a Start point., Inmrmediate points, a Goal point and possibly a Means of movemant. #The semantic routine must translate syntactic entities such as Subjec~ Objecr ana Complements into these roies, i.e. bind the variables. It must then relate them'iii iippropriate ways-- AGENT'IN VEHICLE.</Paragraph> </Section> <Section position="10" start_page="23" end_page="23" type="metho"> <SectionTitle> VEHICLE AT STARTPOINT, VEHICLE'ON MEDIUM, etc.--for each of its temporal </SectionTitle> <Paragraph position="0"> states and call the graphics sysrern to display them.</Paragraph> <Paragraph position="1"> Support is a. verb that describes a static Single state of afEUa.irs in &quot;The world is supported on a turtle's back&quot;. The verbs &quot;balance&quot;, '.'support&quot;, ''stand'' &quot;huld&quot;. are each as-sociated with the semantlc tunct ion SUYPUK'I'~. When a VP constituent using one oi these verbs is completed, SUPPORT1 is called to compute a m,odel of the situation described.</Paragraph> <Paragraph position="2"> SUPPORT1 binds the cases TH1, TH2, SUPPORTPTl, BALPT2. TH stands for THEME and the other two cases ror Support Point, and Balance Point. The following diagram shows the spatial rela tion:. signifie.d by these cases: Thl supports TM2 on its BALPT2 on/with/in his SUPPORTPTl. If these four arguments are bound. the suqport relation is completely defined.</Paragraph> <Paragraph position="3"> If not.</Paragraph> <Paragraph position="4"> means are taken to till in. the missing arguments by 2 default logic.</Paragraph> <Paragraph position="5"> SUPPORT1 takes as arguments, SL5J, OBJ, and CflMPS where COWS is a list of complements. SUBJ and OBJ were computed by the VP parser as the subiect and object of the ACTIVE PSom of the clause.</Paragraph> <Paragraph position="6"> The conditions or rules For transforming these syntactic arguments * into semantic roles are as Collows: Additional modifiers may have been present as-in the example sentences: A clown, on his hands balances a pedestal on his head, on its side beside a pole.</Paragraph> <Paragraph position="7"> A ololn with a pole in his hands b'alances on a pedestal.. .</Paragraph> <Paragraph position="8"> The earlier action sf rhe preposition semantic functions will have reduced these additional complements to no more than those ~bown in Edamples 1 and 2. Brief forns such as &quot;A clown balances on hi$ hands&quot; or &quot;A clown holds a pole&quot; 'result in inbomplete bindings from the rules of SUPPORTL. The legitimacy of suqh brief forms iequires a default logic that in the first case assumes that the Ground supports the clown-at a point called TOP of the ground. In the second case, the clovn.'s SWP~RTPT~ for the pole Is bound to hrs hands and the BALPTZ--for the pole-- is bound to the BOTTOH of the pole The verb &quot;hold&quot; puts a default value of &quot;hands&quot; on the structure it passes to SUPPORT^ according to the following definition:</Paragraph> </Section> <Section position="11" start_page="23" end_page="39" type="metho"> <SectionTitle> (HOLD (LAMBDA (ST) </SectionTitle> <Paragraph position="0"/> <Paragraph position="2"> The default logic af the verb seeks these values to bind them appropriately to any-empty case arguments. The more general default values of TOP as a missing SUPPQRTPTl and BOTTOM as a missing BALPT2 and the fact that the object on the bottom of the heap must be supported. by the GROUND are all supplied just prior to constructing a picture frame.</Paragraph> <Paragraph position="3"> The result of SUPPORT1 is to create a process model of the fbllowing form: (Ci TOK balance, GLOBAL (...)-,INIT(...),INTER(...) RESULT ( . . . ) ) The value of the attribute, GLOBAL is a quoted set of (PUT X Y Z) whrch Me true at all times in the model. INIT !is the set of relations true Bt the initial state of time 5n the model, INTER Ts those for the intermadiage states, and R~SULT is the set fbr the final state. When a functionWG for Pragmatics evaluates one of these attributes, the result is to evaluate these PUT functions to produce a semantic network representing the state of affairs at a given instant of time. The semantic relations are translated to ATTACH 4-tuples which then generate a picture of the state. Successive pictures are obtained by calling PRAG repeatedly for INITial, INTERhediate, and RESULT states.</Paragraph> <Paragraph position="4"> For the examples of the SUPPORT1 verb, only the GLOBAL attribute is given values as f~llows:</Paragraph> <Paragraph position="6"> Initial, Intermediate and Result states are null since the verb simply describes a static state.</Paragraph> <Paragraph position="7"> The verb MOVE* is more,complex and more interesting. Let us assume I I as input the-sentence, A clown on his head ;ails frorn'Corno to Menaggio&quot; Wheh the parser has completed its VP the semantic structure.1~ as follows: (abbteviated to the portion relevant to-this discussion.) Thie definition of the canditione for MOVE* is atill incomplete except fox the verb &quot;sail&quot; and will be modified with further experience. Haying bound the role varieblee, MOVE* creates a procese model by assigning to ST, gets of value8 for the attributee GLQBAL, INITlal, INTEBmediate, and,RBULT.</Paragraph> <Paragraph position="8"> Ffg. 3 shows theee states in the form of a process model, When this process model, C3, is evaluated, the function PRAG is called with the arguments C3 and either INIT, INTER, or RESULT. PRAG will first interpret the GLOBAL attribute causing the state represented on the property liste Tor Tokens of clown, boat, etc. to be changed. It will then make the changes indicated by the PUT8 which are additions, and the REMPROPs which are deletions. If PUG ie called three times in succession for INIT, INTER, and RESULT, three euccessive sfatee are created to shaw the progreeeion of the process from etart to finieh. After PRAG has been called the support points and balance pointa are all defaulted as neceeaary to TOPS and BOTTOMs by the function that calls the GRAPHIOS system, This function</Paragraph> <Section position="1" start_page="34" end_page="39" type="sub_section"> <SectionTitle> VI Semantics of Scenes </SectionTitle> <Paragraph position="0"> A scene is composed of a set of Pictures related to each other by adjacency and Support relacions including their poiats of contact. A picture is a LOGO display program that when called with a given start point and heading of the display turtle or cursor will construct a two dimensional line drawing. A square can be drawn by the following sequence of operations. (See Papert 1972.) PENDOWN, FORWARD 20, RIGHT 90, FORWARD 20, RIGHT 90, FORWARD 20, RIGHT 90, FORWARD 20, RIGHT 90, PENUI?.</Paragraph> <Paragraph position="1"> The last ''FIGHT 90&quot; restores the cursor to its original heading. FORWAEU and BACK axe vector making functions that draw a vect'or from the current xy point of the curser a given number of +its in the direction the cursor is aimed' The language uses functions with arguments and may create and call subroutines. Square may be defined as SQUARE;SIZE: FORWARD :SIZE,. . . -.ETC.</Paragraph> <Paragraph position="2"> If a triangle has als~ been defined, we can then define:</Paragraph> <Paragraph position="4"> It is the convenience and simpl~cityeof these LOGO conventi'ons that convinced me that drau3,ng pictures from sentences would not add any gteat complexity to a basic language analysis system.. LOGO offers many additional features as a language for teaching programming skills to non-mathematically oriented users and one of the most important of these may be as a parenthesis-free form of LISP.</Paragraph> <Paragraph position="5"> In our use of LOGO graphics, we-consider that a picture has a name, a program to praw it, a cursot startpoint~value, a head'ing, a size, a frame of minimum arid maximum X and Y coordinates, a center of gravity and coordinates associated with any points on it that we need to refer to, such as feet, hands, head, top, bottom, etc.</Paragraph> <Paragraph position="6"> All of the XY coordinates designated in a picture structure are relative to the startpoint, heading and size. If we set the startpoint to a given value, say 500; 0; the clown will be drawn from the bottom center of the screen. If we set HEADING to 90, it will be drawn on its side. 1f we change size to 2 each vector composing the picture will be twice as long. If we whsh to translate the clown to the right 50 units, 50 is added to the X coordinate of the startpoint. IF we wish to m6ve it up, a number is added to the Y coordinate of the startpoint. If we Wish to rota-te it onto its head with its head at 500,100 life is more difficult. We must use trigonometrikc functions to compute a heaaing value and a location of the startpoint that will achieve this result. A functipn called ORIENT* takes as arguments an object, i-ts balance point, and a reference point.</Paragraph> <Paragraph position="7"> (ORIENT* CLOWN, HEADXY , (500,100)) This function adjusts the startpoint and heading so that the head of the clown will be at (500,100) with the center-of gravity above the point. Siinilar- adjustments are made to the PFRAME values to translate and rotate the imaginary picture frame defined by the XY extremals.</Paragraph> <Paragraph position="8"> To assemble a set of pictpres into la scene, the bottom pieture is assigned an XY s~tartpoint and heading. Each picture it supports is translated and rotated to result in adjustments to startpoint, heading and pframe values. Each picture beside it is s~ilarly adjusked until a scene is cgmpleted by accounting for all its pictures'. At tQis point, the scene is scaled to the size of the display screen, and the picture drawing programs are,executed.</Paragraph> <Paragraph position="9"> The PFRAME concept developed by Gordon Novak and Mike Smith is very helpful as a computafional abbreviation Eor.fhe program that draws the picture. The PFRAME attribute has a minimum x, maximum x, minimum y, maximum y as four points that define a rectangle that surrounds the extreme points of the pictime, When the picture is programmed these are assigned by hand with reference to whatever startpoflt and heading were used. The picture as defined is taken as size 1. Whenever the picture is translated or rotated the values of PFEUME, STARTPT apd HEA~ING are adjusted accordingly. As each pair of piccures are combined into a scene, a FFW is computed for the scene. The final PFRAME for the entire scene is adjdsted to the size of the screen with appropriate scalidg of the size values of its component pic turas.</Paragraph> <Paragraph position="10"> A f'requent use of PFRAMES is to find default values for TOP, BOTTOM, LEFTS~DE and RIGHTSIDE as contact points between pairs of pictures .</Paragraph> <Paragraph position="11"> Dep'ailed descriptions of these processes are-not particularly relevant to this paper's goal of presentipg an easily computable syntactic-semantic scheme for subsets of English but will be presented in forthcoming papers by Bennett-Novak and by Michael Smith.</Paragraph> </Section> </Section> class="xml-element"></Paper>