WORD EXPERT PARSING l 
Steven L. Small 
Department of Computer Science University of Maryland 
College Park, Maryland 20742 
This paper describes an approach to conceptual analysis and understanding of natural language in which linguistic knowledge centers on individual words, and the analysis mechanisms consist of interactions 
among distributed procedural experts representing that knowledge. Each word expert models the process of diagnosing the intended usage of a particular word in context. The Word Expert Parser performs 
conceptual analysis through the Interactlons of tl~e individual experts, which ask questions and 
exchange information in converging on a single mutually acceptable sentence meaning. The Word Expert theory is advanced as a better cognitive model of natural language understanding than the traditional 
rule-based approaches. The Word Expert Parser models parts o~ tSe theory, and the important issues of control and representation that arise in developing such a model \[orm the basis of the technical 
discussion. An example from the prototype LISP implementation helps explain the theoretical results presented. 
\[. Introduction 
Computational understanding of natural language requires complex Interactions among a variety of distinct 
yet redundant mechanisms. The construction of a computer program to perform such a task begins with the 
development of an organizational framework which Inherently .incorporates certain assumptions about the 
nature ot these processes and the environment in which 
they take place. Such cognitive premises affect nro?oundly the scope and substance of computational 
~nalysis for comprehension as found in the program. 
This paper describes a theory of conceptual parsing which considers knowledge about language to be 
distributed across a collection of procedural experts centered on individual words. Natural language parsing 
with word experts entails several new hypotheses about the organization and representation of linguistic and 
pragmatic knowledge for computational language comprenension. The Word Expert Parser \[1\] demonstrates 
hpw the word expert qTt~T~ed w£~h certain ocher choices oaseo on previous work, affect structure and 
process in a cognitive model of parsing. 
The Word Expert Parser is a cognitive model of conceptual language analysis in which the unit of 
ltngu~stic knowledge is the word and the fqcu~ o~ research ts the set or processes unoerlyinR 
comprehension. The model is aimed directly at problem~ of word sense ambiguity and idiomatic expressions, and in 
greatly generalizing the notion of wora sense, promotes these issues to a central place in the study of language 
parsing. Parsing models typically cope unsatisfactorily with the wide heterogeneity of usages of particular 
words. If a sentence contains a standard form of a word, it can usually be parsed; if it involves a less prevalent 
form which has a different part of speech, perhaps it too 
can be parsed. Disti.nguishing amen 8 the ~any senses of a common vero, adjective, or pronoun, tar example, or 
correctly translating idioms are rarely possible, 
At the source of this difficulty is the reliance on rule-based formalisms, whethar syntactic or semantic 
(e.g.. cases), which attempt to capture ~he linguistic contributions inherent in constituent chunks or sentences 
that consist of more than single words. A crucial assumption underlying work on the Word Expert Parser is 
that the ~undamental unit of linguistic Knowledge is the 
word. and that understanding its sense or role in a particular context is the central parsing process. In 
the parser to be described, the word expert constitutes the kernel of linguistic knowled~nd zts representation 
the e~emental data structure. IE is procedural in nature and executes directly as a process, cooperating with the 
other experts for a given sentence to arrive at a mutually acceptable sentence meaning. 
Certaln principles behind the parser d 9 nqt follow directly from the view or worn primacy, out ~rom other 
recent theories of parsing. The cognitive processes involved in language comprehension comprise the focus of 
linguistic study of the word expert approach. Parsin8 is viewea as an inferential process where linguistic 
knowledge of syntax and semantics and general pragmatic 
knowledge are applied in a uniform manner during 
IThe research described in this renor~ .is funded by the National Aeronautics and Space Admzn~stratton under 
grant , n umbe, r NSC-7255. Their support is gratefully acKnowleageG, 
Interpretatlon. This methodological position closely follows that of Rlosbeck (see \[2\] and \[3 \]) and Schank 
\[4\]. The central concern with word usage and word sense ambiguity follows similar motivatlons of Wllks \[5\]. The 
control structure of the Word Expert Parser results from agreqment .with ~he hypothesis of .Harcus that parsing can 
he none aetermzntsttcally and ~n a way tn Dhlcn information ,gained through interpretation is permanent 
\[6\]. Rieger ~ view of inference as intelligent secectlon tmong a number of competing plausible alternatives {7J of 
course forms the cornerstone of the new theory. Hi~ ideas on word sense selection for language analysis (\[8\] 
and \[9~) and strategy selection for general problem solving \[10\] constitute a consistent cognitive 
perspective. 
Any natural language understanding system must incorporate mechanisms to perform word sense 
dlsa?biguatlo~ in. the context .of ape, n-ended world gnow~eoge, rne Importance at these mechanisms tar wore 
usage diagnosis derives from the ubiquity of local ambiguities, and brought about the notion chat ~hey be 
made the central processes of computational analysls an 9 understanding, Consideration of almost any Engllsn 
content word leads to a realization of the scope of the problem -- with a little time and perhaps help from the 
dlctlonaFy , man~.dlstinct usages can ee.id~ntifl~d. As.a stmpie lllustrarzon, several usages earn tar the worus 
"heavy" and "ice" appear in Figure I. Each of. these seemingly" benign words exhibits a rich depth of 
contextual use, An earlier paper contains.a list at almost sixty verbal usages for the word "take" \[llJ. 
The representation of all contextual word usages in an active way t~at insures their utility for linguistic 
dlagnasis led to the notion of word experts. Each word expert is a procedural entit~~f all posslblq 
contextual interpretations of the -word it represents. = Whe~ placed in a context formed by.expqrts for thg.othe ~ 
wares In a sentence, earn expert ShOUld De capaole or sufficient context-problng and self-examination to 
determine successfully' its functional or semantic role, and further, to realize the nature of that function or 
the precise meaning of the word. The representation and control issues involved in basing a parser on word 
experts are discussed below, following presentation of an example execution of the existing Word Expert Parser. 
2. Model Overview 
The Word Expert Parser successfully parses the sentence 
"The deep ~hilosopher throws the peach pit into the aeep pit," 
through cooperation among the appropriate word. experts, Initialization of ~he parser consists or retrlevln~ tr~ 
experts for "the", "deep', "philosopher", "throw", s", ~ 
2An Important aeeumption of the word expert viewpoint is that the set or sucn contextual wars usages is not 
only finite, but fairly small as well. 
3The verspectlve of viewing language through lexlcal contribution~ to structure a~d meaning has naEurallv led 
to the development of wold experts for co~mon m?rphemes that are not war as ~ana even, experimentally, for 
~unctuatlos), Especially important is the word expert tar "-ins', which aids significantly i n helpinR co 
Some word senses of "heavy" 
1. An overweight person is politely called "heavy": 
"He has become quite heavy." 
2. Emotional music is referred to as "heavy": 
"Mahler writes heavy music." 
~. An intensity of precipitation is "heavy": 
"A heavy snow is expected today." 
Some word senses of "ice" 
I. The solid state of water is called "ice": 
"Ice melts at 0Oc. " 
2. "Ice" participates In an idiomatic neminal 
describing a favorite delight: 
"Homemade ice cream is delicious." 
3. "Dry Ice" is the solid state of carbon dioxide: 
"Dry ice will keep that cool ;11 day." 
~. "Ice" or "iced" describes things that have been cooled (sometimes with ice): 
"One iced tea to go please." 
5. "Ice" also describes things made of ice: 
"The ice sculptures are beautiful~" 
6,7. "Ice hockey" is the name of a popular sport which 
has a rule penelizln~ an action called "icing": 
"Re iced the puck causing a face-off." 
~. The term "ice box" refers to both a box containing ice used for cooling foods end a refrigerator: 
"This ice box isn't plugged in~" 
Flsure 1: Example contextual word usages 
".over", and ~o forth, from a dis~ flle~ and .or~anizin 8 them along with data repositories cal~e~ wor~ oIns in a 
left to right order in ~he sentence level wo~k~pace. Note that three copies ot t T~-3R~...t ~or "the" anb c.~o 
cop.ies of each expert for "deep" and "pit" appear in th~ worKspace. Since each expert executes as a process, 
each process Inetantlatlon in the workspa..ce must be put into an executaole state. At this point, the parse is 
ready to begin. 
The word expert for "the" runs first, and is able to terminate immediately, creating a new concept designator 
(called a concept bin and participating in the concept level worksp~f~"~iclT-'will eventually hold the data 
the intellectual philosopher described in the 
input. Next the "deep" expert runs, and since "deep" has 
a number of word senses,5 is unable to terNinate (i.e~, 
complete its dlscriminetlgn task)..Instead,it ~uspenas its execution, stating the conditions upon winch it 
should be resumed. These conditions take the form of associative trigger patterns, and are referred to as 
disambiguate expressions Involving gerunds or participles such as "the man eat ir~ tiger". A full discussion ot 
thls will appear in \[12\]. 
4Al~hough I call them "processes". word experts are actually coroutlnes resembling CONNIVER's generators 
\[tS\], and even more so, the stack groups of the MIT L~SP Machine \[14\]. 
51t should be clear that the notion of "word sense" as used here encompasses what might more traditionally be 
~escr.ibea as "contextua~ ~orn usage", Aspects o~ a word token's linguistic envlromnent constitute Its broadened 
"sense". 
restart demons. The "deep" expert creates .a restart demon co wake l'C up when the sense ot the nominal to its 
right ( l .e., "~hllosopher") becomes knoWn. The exper~ 
f.or "philosopher now runs, observes the co.ntrol state ot the parser, ant contributes the tact Chat One new concept 
refers to a person e.ngaged in the study of philosophy. As this expert terminates, the expert tot "=eep" resumes 
spontaneously, and, constrained by the fact chat "deep" must describe an entity that can be viewed as a person, 
it finally terminates successfully, contributing the fact that the person is intellectual. 
The "throw" expert runs next and successfully prunes away several usages of "throw" for contextua, reasons. A 
major reason for the semantic richness of verbs such as 
"throw", "cake", and "Jump", is that In context, each interacts strongly with a number of succeedin8 
pre~ositions and adverbs to form distinct meaninBs, The woro expert approach easily handles this grouping 
together or words to torn larger word-like entities. In the particular case of verbs, the expert for a word like 
."throw" simply exam.ines.i~.s rSght lex ical n.eighbor, an~ oases its oWn sense alscrtmlnet2on on the co(Rolnetlon or 
~ at it .expects co find there, what It actually finds ere, an~ what this neighbor tells it (if It Soas so rat 
as to ask). No interesting p.article follows throw" in the current exampze, out It snoulo oe easy to conceive or 
th.e basic expert probes to discriminate the sense of "throw" wnen ;ol-owed by "away", "up", "out" ~ "in the 
towel", or other woras or wore groups, when no such word rollows "throw". as Is the case nere, its expert slmp-y 
waits for the existence of an entire concept to Its 
right, to determine if it meets any of the requirements .~hat would make the correct contextual interpretation of 
' throw" different trom the expected "propel by moving 
ones arm" (e.g., "throw a party'.'). Before any such substantive conceptual activity takes place~ however, .t~ 
"S" expert ~uns arm ~ontri~uCes Its stannaro morphological information to throw "s data bin. This 
execution of the "s" expert does not, of course, affect 
"throw"' s suspended status. 
The "the" expert for the second "the" in the 
sentence runs next, and as in the previous case, creates a new con.cep~ bin to represent the da.~a about the no nina~ 
and des crlptlo.n, to come. Lne "peecn" expert realizes that It coulo oe either a noun or an adjective, and thus 
attempts what ~ call a "pairing" operation with its right neighbor. It essentially asks the expert for "pit" if 
the two ot them form a noun-noun pair. To determine the answer, ooth "pit" and "peach" have access to the entire 
model of linguistic and pragmatic knowledBe. Durtn~ this time. ~peach" is in a st.a~e called "attempting pairing" 
which Is nlzrerent trom the "suspended" state of the "throw" ex.~.ert. "Pit" answers back that it does pair up 
with "peach' (since "pit" is aware of its run-time 
context) and enters the "rea.dy" state. "Peach".now ned:ermines its c.orre~t sense and t;erm~netee: An.d ~nc~ 
only one mean%ngrul sense ~or'plt remains, the pit expert executes quickly, . t.ermlnattng with the 
contextually a~pro~riace "trulC pit" sense. As ic terminates, the piC. expert closes off the concept b.in 
In which It part~cipaces, spontaneously resumins the 
"throw" expert. An examination of the nature of fruit pit.a reveals that they are pergect.ly suited to propelling 
with ones. arm, ar~ thus, the "th.row" expert terminates successzul~y, contributing its wore| sense to its event 
concept bin. 
.The "lnto~ expert, runs next, opens a concept bin ~of t~pe 'setting") rot the time, location, or situation 
about to be described, and suspends itself. On suspension, "lnto"'s expert posts an associative restart 
condition that will e.nable .its re.sumptlon when a new p~cture concept ~s opened to the right. This initial 
action CaKes p~ace rot most prepositions. In certain cases, if the end of a sentence is reached before an 
appropriate expected concept is opened, an expert will 
take alternative action. For example, one of the "in" 
experts restart trigger patterns consists of control state data of Just this kind -- if the end of a sentence 
is rear.had .and no. conceptuql object, for the sect.ing creaceo oy "In" has oeen round, the "in" expert wxl~ 
resume nonetheless, and create a default concept t or perform some kind of intelligent reference aeterminatlon. 
The sentence "The doctor is In." illustrates this point. 
In the current example~ the. "the" expert that executes lm.med~ately alter t_.nto"'s suspension creates 
the exporter.picture concept. The wor.d ex~er~..for."deep" then rune ano, as oe~ore, cannot Immedlately olscrlmlnate 
among Its several se.nses. ."Deep" chug suspend.s, waiting tor the expert rot the word to Its right to neap. At h.ls 
point, there are two experts suspended, although ~.ne control flow remalns ralrly simple, other examples exist 
in whlch a complex set or conceptual dependencies cause a number or exper.~s to De suspendedslmultaneously. These 
situations usuaA.~y resolve themes+yes wl~_h a ca§qadlns o~ expert res,-,ptlons and terminations. In our seep ~c 
example, "deep" ~oets expectations on the central tableau of global control state Knowledge, and waits rot "pit" to 
terminate • "PIt"' s expert now runs, and since thls 
10 
bulletin board contains "deep"'s expectations of a ~. oI~, or printed matter, "pit" maps immediately 
onto a large hole in the ground. This in turn, causes both the resumption and termination of the "deep" expert 
as well as the closure of the concept bin to whlch the~ oelong. At the closing of the concept bin, the "into 
expert resumes, marks its concept as a location, and 
terminates. With all the word experts completed and all concept bins closed, the expert for ".'" runs and 
completes the parse. The concept level workspace now contains five concepts: a picture concept designating an 
intellectual philosopher, an event concept representing the throwing action, another picture concept describing a 
fruit pit which came from a peach, a setting concept representing a location, and the picture concept which 
describes precisely the nature of this location. Work on the mechanism to determine the schematic roles of the 
concepts has just begun, and is described briefl~ later. A program trace that shows the actions ot the Nora Expert 
Parser on the example just presented is available on request. 
3. Structure of the Model 
The organization of the parser centers around data repositories on two levels -- the sentence level 
workspace contains a word bin for each word (and sub-lexical morpheme) of the input and the concept level 
workspace contains a concept bin (described above) for each concept referred to in the input sentence. A third 
level of processing, the schema level workspaee, while 
not yet implemented, will contain a schema for each conceptual action of the input sentence. All actions 
affecting the contents of these data bins are carried out by the word expert processes, one of which is associated 
with each word bin in the wo rkspace. In addition to this 
first order information about lexical and conceptual objects, the parser contains a central tableau of control 
state descriptions available to any expert that can make 
use of self referential knowledge about its own processing or the states of processing of other model 
components. The availability of such control state information improves considerably both the performance 
and the psychological appeal of the model -- each word expert attempting to disambiguate its contextual usage 
knows precisely t~e progress of its neighbors and the 
state of convergence (or the lack thereof) of the entire parsing process. 
Word Experts 
The principal knowledge structure of the model is the word sense discrimination expert. A word expert 
represents the the linguistic knowledge required to dlsamblguate the meaning of a single word in any context. 
Although represented cumputationslly as coroutlnes, these experts differ considerably from ad hoc LISP programs and 
have approximately the same ~elatlon ~o LISP as an augmented transition network \[15\] grammar. ° 2use as rh~ 
graphic represeptatlon of an augmented transltlon networ~ aemonstrates the basic control paradigm of the ATN 
parsing approach, a graphic representation for word experts exists which embodies its functional framework. 
Each word expert derives from a branching discrimination structure called a word sense discrimination network or 
sense net. A sense nec consists of an ordered se~ of • /~tr~Ti~g (the nodes of the network), and for each one, 
the set of possible answers to that question (the branches emanating from each node). Traversal of a sense 
network represents the process of converging on a single contextual usage of a word. The terminal nodes of a 
sense net represent distinct word senses of the word modeled by the network. A sense net for the word "heavy" 
appears in part (a) of Figure 2. Examination of this network reveals that four senses are represented -- the 
three adjective usages shown in Figure 1 plus the numinal sense of "thug" as In "Joe's heavy told me to beat it." 
Expert Representation 
The network representation of a word expert leaves out certain computational necessities of actually using 
it for parsing. A word expert has two fundamental activities. (I) An expert asks questions about the 
lexical and conceptual data being amassed by its neighbors, the control states of various model 
components, and more general issues requiring common 
sense or knowledge of the physical world. (2) In addition, at each node an expert performs actions to 
affect the lexical and conceptual contents of the workspaces, the control states of itself, concept bins, 
6An ATN without arbitrarily complex LISP computations on each arc and at each node, that is. 
7In addition to common sense knowledge of the physical world, this could include information about the plot, 
characters, or focus of a children's story, or in a specialized domain such as medical diagnosis \[17\], could 
include highly domain specific knowledge. 
and the parser as a whole, and the model's expectations. The current procedural representation of the word expert 
for "heavy" appears as part (b) of Figure 2. 
Each word expert process Includes three components -- a declarative header, a start node, and a 
body. The header provides a description of the expert's 
behavior for purposes of inter-expert constraint 
forwarding. If sense discrimination by a word expert results in the knowledge that a word to its right, either 
not yet executed or suspended, must map to a specific 
sense or conceptual category, then it should constrain it to do so, thus helping it avoid unnecessary processing or 
fallacious reasoning. Since word experts are represented as processes, constraining an expert consists of altering 
the pointer to the address at which it expects to continue execution. Through its descriptive header, an 
expert conditions this activity and insures that it takes place without disastrous consequences. 
Each node in the body of the expert has a type 
deslgnated by a letter following the node name. either Q (question), A (action), S (suspend), or T (terminal). By 
tracing through the question nodes (treating the others 
as vacuous except for their gore pointers), a sense network for each word expert process can be derived. The 
graphical framework of a word expert (and thus the questions it asks) represents its principal linguistic 
task of word sense disamblguatlon. Each question node has a type, shown following the Q in the.node -- MC 
tmultiple choice), C (conditional), YN (yes/no/, and PI (posslble/Imposslble). In the example expert for 
"heavy", node nl represents a conditional query into the state of the entire parsing process, and n?de n\[2 a 
multiple choice question involving the conceptual nature of the word to "heavy"s right in the input sentence. 
b Multiple choice questions typically delve into the aslc relations among ob3ects ann actions zn the world. 
For example, the question asked at node n12 of the "heavy" expert is typical: 
"Is the object to my right better described as an artistic object a a form of precipitation, or 
a physical object? 
Action nodes in the "heavy" expert perform such tasks as 
determining the concept bin to which it contributes, and pqstin 8 expectations for the word to its right. In terms 
ot its side effects, the "heavy" expert is fairly simple. A full account of the word expert representation language 
will be available next year \[12\]. 
Expert Questions 
The basic structure of the Word Expert Parser depends principally on the role of individual word 
experts in affectlug.(1) each other:s actions and ~2) the neclaratlve result or computatlonal analysis. ~xperts 
affect each other by posting expectations on the central bulletin board, constraining each other, changing control 
states of model components (most notably themselves), and augmenting data. structures in. the workspeces. ° .They 
contribute to the conceptua£ ans ecnematlc result ot toe 
parse by contrlbuting object names, descrlptions~ schemata, ane other useful data to the concept level 
workspace. To determine exactly what contributions .to make, i.e.j the accurate ones In the particular run-tlme 
context at handj the experts as~ questions ot various kinds about the processe sot the model and the world at 
large. 
Four types of questions may be asked by an expert, and whereas some queries can be made in more than one 
way, the several question types solicit different kinds of information. Some questions requlre fairly involved 
inference to be answered adequately, and others demand no more than simple register lookup. This variety 
corresponds well, in my opinion, with human processing 
involved in conceptual analysis. Certain contextual clues to meaning are structural; taking advantage of them 
requires solel~ knowledge of the state of the parsing process (e.g., 'building a noun prase"). Other clues 
subtly present themselves through more global evidence, usually having to do with linking together high order 
information about the specific domain at hand. In story 
comprehension, this involves the plot, characters, focus of attention, and general social psychology as well as 
common sense knowledge about the world. Understanding texts uealing with specialized subject matter requires 
knowledge about that particular subject, other subjects related to it, and of course, common sense. The 
questions asked by a word expert in arriving at the correct contextual interpretation of a word probe sources 
of both kinds of information, and take different forms. 
8The blackboard of the Hearsay speech understanding system \[~6\]. ~s anelggous to the entire wormspace ot the 
parser, xnoluaxng the word bins, concept bins, and oulletin board. 
ii 
(~ 's the current~ oncept of type) "viceure"? / 
yes ~ 
es the word on~ right contribute 
to the current / 
,concept? ,/ . 
Is the current conceptual object I 
better described/ as arc, e phyeob$,~ 
SERIOUS-OR- INTENSE- EMOTIONAL 0UANTITY MASS 
THUG 
LARGE-PHYS ICAL- 
(a) Network representation of "heavy" expert 
\[word-expert heavy 
<header category (PA • nl)\] 
~sense <descriptors (LARGE-PHYSICAL-MASS . nil) (INTENSE-~UANTITY . nO3) 
(SERIOUS-OR-EMOTIONAL . uS2)>\]> 
<start nO> 
<exnert \[n~:A (~E~USE) 
(NEXT nl)\] \[nl:~ C parser-state t 
(open-picture . n2) 
\[rS:A (CONCEPT new PICTURE) ~rr .4 \] 
(NEXT nlO)\] \[nlO:A (EX~C~(EX~R~ (r,,)Cr") vio,/pp~ie~P~ p~cART)I~ZnTZON) 
~EX~C"I' (rw) view/PP I~¥SOBJ) (N~XT nil)\] 
\[nll:S wait-for-r~lght-word ~RES_U_ME.~trlgger 'expert-state 
(ha) 'terminated)) ~u~u~ t~rst) 
(NEXT nl2)J tel2:0 HC vlew/PP (rw) 
tart . ritz) ~. ~praclpitation~ 
nc~) ~pnysobJ . ntl)I 
\[ntl:T P~ LARGE-PRYSICAL-MASS\] \[nt2:T PA SERIOUS-OR-EMOTIONAL\] 
\[nCS:T PA INTENSE-AMOUNT\]>\] 
(b) Process representation of "heavy" expert: 
Figure 2: Word expert representation 
The explicit representation of control state and structural Informeclon racilltates i~s use in pars in~.-- 
conditional and yes/no questions petters s~'nple lookup operatlona In the PIAN~ER-IIke associative dac~ base \[18\] 
chef stores the workapace data. ~uestlons about the plot or a story or ice cheracfiers, or common sense queetlona 
requLrtn~ spatial or temporal stmul, attona ~}re, bes.C pnrasee as possible/impossible ~or yes/no/maybe) 
q~est$on~, Sometimes during sena~ 4iscrtm~n~tion,. thq p-ausl~illty or some gene.ra~ tgcC~eaus to tee pursult or 
~ifferent Information than Its lmpzauatbtlity. Such aline t lone occur with enough frequengy to justify a 
spec~a~ type or questlon to ueal wtth them. 
The Importance of HulClple Choice 
Multiple choice questions comprise the central inferential component of word experts. They derive from 
R1eger' s notion that intelligent selection among competin 8 alternatives by . relative .differencing 
represents an important aspect oz human proe~em so~vlr~ \[7\]. The Word Expert Parser, unlike certain standardized 
tests, prohibits multiple choice questions from contalnlnR a "none of the above" choice. Thus, ehey 
demand tee most "reasonable" or "consistent" choice of pot ential.ly .unep~ealt.ng answers. What does a child (or 
adult) GO wnen zacea wlcn a sentence that seems Co state. an implausible proposition or reference lmplauqible 
objects? He surely does his best Co make sense ot the 
sentence, no master what ie says. Depending on the 
context, certain intelligent and literate people create metaphorical interpretations for such sentences. The 
word expert approach interprets metaphor, idiom s and "normal" text wleh the same mechanism. 
Multiple choice questions make this possible hut anewe ring them may require tremendously complex 
processing, A substantial knowledge representation zormalism based on semantic networks, such as ~RI. (191, 
with mulclple perspectives, nrocedural attachment, and intelligent aescripCion matching, must be used to 
represent in a uniform way both general world knowledge and knowledge acguired through textual Interprecatlon. 
In KRL terms, a multiple choice question such as "Is the object RAIN more llke ARTISTIC-OBJECT, PHYSICAL-OBJECT, 
or PRECIPITATION?" must be answered by appeal co ~he units representing the four notions involved. Clearly, 
RAIN can be viewed as s PHYSICAL-OBJECT; much less so as an ARTISTIC-OBJECT. However, in almost all contexts, 
RAIN is closest conceptually to PRECIPITATION. Thus, this should be the answer. This multiple choice 
ge;~antsqa I~tS many uses ~n c onceptuaJ~, parslng ar~. :ul~Tscale lanEuage comprene~Jlon as we~ as lngenera- 
problem, solvln K \[201. That any rraEment ot text (or ocher n, lan sensual input) has some interpretation from 
the.point of vi.ew o.~ a parcicula.r read.st constitutes, a zunaamenta~ unaerly~ng ~dea oz the worn expert approacn. 
Exper~ Side Effects 
Word experts take two klnds of actions -- actions 
explicitly intended to affect sense discrimination by other experts)end actions to eugme`nC the conceptual 
infgrmaCion .chat constitutes the result or a parse. Each path throuKn a sense network represents a distinct usage 
of ~he modeled wordt and at each seep of the way, the ~orcl expert must update, the model Co r efle.ct the .state_of 
~Cs processln 8 end t~e extent of 1is Kno.wieoge.. lee heavy" ~per~ of Figure 2(b) exhibits severaA o~ these 
actions. Nodes n2 and ~ of this word expert process represent."heavy"' s decision about the concept bin (i.e., 
;pnceptua, notion) in which It partlclpates. I~. the first case. It declaes Co contribute to tile same Din as 
its left neighbor; in the second, it creates a new one, eventually. \[o cunts.in the conceptual data provided by 
l~.sml~.ana ~ernape ocher experts to its r1.sht.. At node nius heavy posts Its expectations regarolr~ the word to 
ice right on the. central .bulletin board. When it tampora~'ll),, suspect, s execution at none nil, its 
"`suepand. ed' control state description also appears on cnls taD.Leeu, 
.Contro..~ state descriptions such. as "suspended"~ terminates' , "attempting. ~airing" Ls.ee above) ~ and 
"reaay" are posies on this ou~etin board, whlcn contains a state designation for each expert and concept in the 
workJpmce, as well as a description of the parser state a~ a whole. Under res~.rioted condLCions~ an expert may 
arzect the state oeecrlptione on thls tao~eau, an expert that has determined its nominal role, may, for example, 
chan~e the .state of. its.concept .~the one to which lC contributes) to "oounaea" or ' closed", depending on 
whether or. not all or.her experts participating in chat concept nave ce~inated. Worn experts .may post 
expectations, on the bulletin .board co .tacilitace handshaking oetween themselves an~ SUDsequently executing 
neighbors. In the example .parse; the "de`ep" expert expects an entity t~aC It can uescr~oe; oy saylng so In 
de~ail,..~t e mi.bles the "pit" exper~ Co eermloaCe succeseru.lly on flrst runn1~, somethln8 1c would not ~e 
able to do other~r~se. 
The .initial execution of a word. expert _ must accomplien certain goa~s or a structura± nature. It tee 
word participates ~n a noun-noun pa~r, thls must be determined; in either case, the expert must determine the 
concept bin to which it concribucAs all of its descriptive data throughout the parse. ~ 
This concept 
9An exce.pcion arises when an expert.creates a default concept bln to. represent .a conceptua-.notion references 
in tile texts out CO whlcn no woras in the text contribute. The automobile in "Joanie parked." is an 
example. 
12 
could either be one that already exists in the workspace or a new one created by the expert at the time of its 
decision. After deciding on a concept, the principal 
role of a (content) word expert is to discriminate among the possibly many remaining senses of the word. Note 
that a good deal of this disambiguation may take place during the initial phase of concept determination. After 
asking enough questions to discover some piece of conceptual data, this data augments what already exists 
in the word's concept 5in, including declarative structures put there both by itself and by the other 
lexical participants in that concept. The parse completes when each word expert in the .workspace nas 
terminated. At this point, the concept ievez worKspace contains a complete conceptual interpretation ot the 
input text. 
Conceptual Case Resolution 
Adequate conceptual parsing of input text regulres a stage missing from this dlscusslon and constituting the 
current phase of research --- the attachment of each picture and setting concept (bin) to the appropriate 
conceptual case of an event concept. Such a mechanism can be viewed in an entirely analogous fashion to the 
mechanisms just described for performln 8 local disamblguation of word senses. Rather ~han word experts, 
however, the experts on this level are conceptual in nature. The concept level thus becomes the main level of 
activity and a new level, call it the schema level workspace, turns into the ma~n repository rot inferred 
Information. When a concept bin has closed, a concept expert is retrieved from a disk file, and initialized. 
If it is an event concept, its function is to fill its conceptual cases with settings and pictures; if it is a 
setting or picture, it must aetermlne its schematic role. The activity on this level, therefore, involves higher 
order processing than sense discrimination, but occurs in Just about the same way. The ambiguities involved in 
mapping known concepts into conceptual case schemata appear identical to those having to do with ma2ping words 
into concepts. Discovering that the word "pit maps in a certain context to the notion of a "fruit pit" requires 
the same abilities and knowledge as realizing that "the red house" maps in some context to the notion of "a 
~ocation for smoking pot and listening to records". The implementation of the mechanisms to carry out this next 
level of inferential disambiguation has already begun. It should be quite clear that this schematic level is by 
no means the end of the line -- active expert-baseo p~ot following and general text understanding flt nicely Int? 
the word expert framework and constitute its loglca~ extension. 
4. Summary and Conclusions 
The Word Expert Parser is a theory of o rganization and cgntro ~ for a conceptual, lansuage an@.~yzer. Th~ 
contro~ envlrosment ts cnaracter~zeo ny a co£~ectlon ot generator-like coroutines, called word experts, which 
cooperatively arrive at a conceptual interpretation of an ~nput sentence. Many torms of linguistic ann 
non-lln~uistlc knowledge are available to these experts 
In performing their task, including control state Knowledge and knowledge of the world, and by eliminating 
all but the mpst persistent forms of ambiguity, the parser models numan processing. 
This new model of parsin£ claims a number of theoretical advantages: (I) Its representations of 
linguistic knowledge reflect the enormous redundancy in 
natural languages -- without this redundancy in the model, the inter-expert handshaking (seen in many..forms 
in the example parse) would not be possible. ~z) ~ne model suggests some interesting approaches to language 
acquisition. Since much of a word expert's knowledge Is encoded in a branching discrimination structure,, addlng 
new information about a word involves the addition oz a new branch. This branch would be placed in the expert at 
the point where the contextual clues for dlsambiguatlng 
the new usage differ from those present for a known usage. (3) Idiosyncratic uses of langua8@ are easily 
e ncooea, s~nce the wore expert provides a c~esr way to no so. These uses are indistinguishable from other uses in 
their encodings in the model. (4) The parser represents a cognltively plausible model or se~uentlal 
coroutine-like processing in human ~anguage 
understanding. The organization of linguistic knowledge around the word, rather than the rewrite rule, motivates 
interesting conjectures about the flow of control In a human language understander. 
ACKNOWLEDGEMENTS 
I would llke to thank Chuck Rieger for his Insights, encouragement, and general manner. Many of the ideas 
presented here Chuck has graciously allowed me to steal. 
In addition, I thank the following people for helpin 8 me with this work through their comments and suggestions: 
Phil Agre, Milt Crlnberg, Phll London, Jim Reggla, Renan 
Samet, Randy Trigg, Rich Wood, and Pamela lave. 
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