| { |
| "paper_id": "J75-4011", |
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| "date_generated": "2023-01-19T02:40:21.636128Z" |
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| "title": "", |
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| "first": "Perry", |
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| "T" |
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| "last": "Miller", |
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| "institution": "Massachusetts Institute of Technology Cambridge", |
| "location": { |
| "postCode": "02139", |
| "region": "Massachusetts" |
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| "abstract": "\\Jheh a user interacts with a natural language system, he may well use words and expressions which were not anticipated by the system designers. This paper describes a system which can play TIC-TAC-TOE, and discuss the game while it is in progress. I f the system encounters new words, new expressions, or inadvertent ungrammaticalities, it attempts t o understand what was meant, through contextual inference, and by asking i h t e l i i g e n t clarifying questions of the user. The system then records the meaning of any ne9 words or expressions, thus augmenting its 1inguist;lic knowledge i n the course of user interaction, A number o f systems tire being developed which communicate with users i n a natural language such as English. The ultimate purpose of such systems is t o provide easy computer access to a technically Onsophisticated pepon. When such a person interacts with a natural language systemr, however, he is quite likely t o use words and expressions which were not anticipated. To provide truly natural interaction, the system should be able t o respond intelligently when this happens. Most current systems, such as those of Winograd [ l o ] and Woods I l l ] , are not designed t o ;ope with such \" l i i g u i s t i c i n p u t uncertainty.\" Their parsers f a i l completely i f an i n p u t sentence does not use a s p e c i f i c , b u i l t-i n syntax and vocabulary. A t the other extreme, systems l i k e ELIZB [93 and PARRY [ Z ] allow the user to type anything, but make no attempt t o fully understand the sentence. The present work explores the tnlddle ground between these extremes: developing a sys.t;em which has a great deal of knowledge about a particular subject area, and which can use this knowledge t o make language interaction a flexible, adaptive, learning medium. In pursuing t h i s goal, the present work i s most closely related t o work being dona i n the various speech recognition efforts [5, 7, 8, 121 which ara studying how l i n g u i s t i c and semantic constraints can h e l p deal w i t h the ACOUSTIC error and uncertainty of speech. The adaptive system, however, is designed t o deal with a much mors LINGUISTIC type of uncertainty. When people use unfamiliar words or expressions in conversation, we can usually deduce from context what is meant, and i f not, we can a t least ask i n t e l l i g e n t clarifying q u~s t i o n s. To allow the machine t o do the same, there must be a very flexible interaction of syntax and $emantics i n the parsing/understanding process, There must be a d i f f e r e n t parser organization, and a more f l e x i b l e use oP l i n g u i s t i c and semantic c o n s t r a i n t s , than i s p f e s e n t in current natural language sys tern. The adaptive system is a step towards t h i s goal Tha c u r r e n t implementation i s a prototype, design'ed to i l l u s t r a t e many o f these ideas, and t o t i e them together in a restricted system t h a t is complete", |
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| "paper_id": "J75-4011", |
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| "abstract": [ |
| { |
| "text": "\\Jheh a user interacts with a natural language system, he may well use words and expressions which were not anticipated by the system designers. This paper describes a system which can play TIC-TAC-TOE, and discuss the game while it is in progress. I f the system encounters new words, new expressions, or inadvertent ungrammaticalities, it attempts t o understand what was meant, through contextual inference, and by asking i h t e l i i g e n t clarifying questions of the user. The system then records the meaning of any ne9 words or expressions, thus augmenting its 1inguist;lic knowledge i n the course of user interaction, A number o f systems tire being developed which communicate with users i n a natural language such as English. The ultimate purpose of such systems is t o provide easy computer access to a technically Onsophisticated pepon. When such a person interacts with a natural language systemr, however, he is quite likely t o use words and expressions which were not anticipated. To provide truly natural interaction, the system should be able t o respond intelligently when this happens. Most current systems, such as those of Winograd [ l o ] and Woods I l l ] , are not designed t o ;ope with such \" l i i g u i s t i c i n p u t uncertainty.\" Their parsers f a i l completely i f an i n p u t sentence does not use a s p e c i f i c , b u i l t-i n syntax and vocabulary. A t the other extreme, systems l i k e ELIZB [93 and PARRY [ Z ] allow the user to type anything, but make no attempt t o fully understand the sentence. The present work explores the tnlddle ground between these extremes: developing a sys.t;em which has a great deal of knowledge about a particular subject area, and which can use this knowledge t o make language interaction a flexible, adaptive, learning medium. In pursuing t h i s goal, the present work i s most closely related t o work being dona i n the various speech recognition efforts [5, 7, 8, 121 which ara studying how l i n g u i s t i c and semantic constraints can h e l p deal w i t h the ACOUSTIC error and uncertainty of speech. The adaptive system, however, is designed t o deal with a much mors LINGUISTIC type of uncertainty. When people use unfamiliar words or expressions in conversation, we can usually deduce from context what is meant, and i f not, we can a t least ask i n t e l l i g e n t clarifying q u~s t i o n s. To allow the machine t o do the same, there must be a very flexible interaction of syntax and $emantics i n the parsing/understanding process, There must be a d i f f e r e n t parser organization, and a more f l e x i b l e use oP l i n g u i s t i c and semantic c o n s t r a i n t s , than i s p f e s e n t in current natural language sys tern. The adaptive system is a step towards t h i s goal Tha c u r r e n t implementation i s a prototype, design'ed to i l l u s t r a t e many o f these ideas, and t o t i e them together in a restricted system t h a t is complete", |
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| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "The best way t o introduce the system is t o show i t in o p e r a t i a n .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "SAHPLE SESSION", |
| "sec_num": "2." |
| }, |
| { |
| "text": "In the sample session that follows, user input is pteced~rd by 'U:\", ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "SAHPLE SESSION", |
| "sec_num": "2." |
| }, |
| { |
| "text": "(1) to explore the techniques required t o achieve adaptive behavior, and ( 2 ) t o h e l p fornulate the issues which will have t o be faced when incorporating these techniques i n t o a much broader natural language system. An interesting aspect o f this approach is t h a t t h e clause-level syntax is entirely domain-independent. I t knows no thing about TIC-TAC-TOE, o r even about the words used t o talk about TIC-TAC-TOE. Tke surface frames allow semantics t o t a l k t o syntax purely in terms o f syntactic labels. As a result, one could write a single syntactic module, and t h a n insert i t unchanged i n t o many domains.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The present simple system h a s been developed w i t h two goals in mind:", |
| "sec_num": null |
| }, |
| { |
| "text": "can be used when processing a sentence. replies t h a t t h e sentence follows normal order. Had the string been \"verb obj pp\" syntax would reply t h a t the subject had been deleted. I f the s t r i n g was @'do agent verb obj p p n , syntax would reply that subjectverb inversion had taken p l a c e . Given \"gent obj verb ppn, syntax would reply that t h e object was out of position.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ". 1 . 1 Using t h i s Information In t h i s s e c t i o n , we describe i n more d e t a i l how t h i s knowledge", |
| "sec_num": "4" |
| }, |
| { |
| "text": "For instance, if syntax r e p l i e s t h a t the object is out of position i n the clause, or t h a t there is incorrect agreement in number between subject and verb, t h e system may decide that t h e user has made a minor grammatical error, and allow the sentence t o be processed anyway, especially if there i s no better interpretation of the sentence. In this", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Thus syntax i s s e t up to notice both g~irnmcatical and u n g r m a t f cal permutations i n constituent order, and t o comment appropriately. The system must then decide how t o interpret these comments.", |
| "sec_num": null |
| }, |
| { |
| "text": "( 2 ) If a constituent is unknown:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "way, clause-level syntax plays an a s s i s t i n g role rather than a castrolling r o l e i n t h e analysis o f a sentence.", |
| "sec_num": null |
| }, |
| { |
| "text": "If an unknown constituent is p r e s e n t , then both the frame and slot information can be used to h e l p resolve its meaning. For i n s t a n c e , suppose the sentence is \" I place a c r o s s in the canter squarew, and The system can then ask if \"to plunk something somewherew means \" t o place something somewheren, and upon getting an affirmative reply, can add t h e new frame to those associated w i t h the concept PLACE. These syntactic features, however, need not bs inflexible rules. Sentence understanding can still psocaed w e n i f tha syntactic features found by syntax do not exactly match those specified by the clausefunction frame. Thus, an inadvertent ungrammaticality cam readily be recognized as such, and processing can continue. ", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 176, |
| "end": 224, |
| "text": "\" I place a c r o s s in the canter squarew, and", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "way, clause-level syntax plays an a s s i s t i n g role rather than a castrolling r o l e i n t h e analysis o f a sentence.", |
| "sec_num": null |
| }, |
| { |
| "text": "(1) The number of concepts a v a i l a b l e t o the system a t present is very small. T h i s , in fact, is why the system's first guess is usually the correct one. I f the sentence is at a l l w i t h i n the systea's comprehension, t h e options as to its meaning a r e currently q u i t e limited.( 2 ) The range of expressive devices presently recognized is q u i t s limited as well. For instance, the system does n o t recognaze relative clauses, con junctions, o r pronouns (except f o r 1 and you).( 3 ) The system currently d e a l s only with TOTALLY UNFMILIAR words and expressions in this adaptive fashion, It w i l l not correctly handle familiar words which are used in new ways (such as a noun used eas a varb, as i n wzero the c e n t e r s q u a r e n ) .( 4 ) The system t r i e s to map the meaning o f new wards and expressiuns into i t s s p e c i f i e d s e t of underlying concepts. It then displays its hypotheses t o the user, g i v i n g him only the option of saying yas or nu. The user cann-ot say \"no, not q u i t e , it meahs . . .\". (Thus concepts like V h e 'northeast1 square\" o r \"the 'topmost' squarew would ba confusing and not correctly understood.)", |
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| "section": "", |
| "sec_num": null |
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| "FIGREF0": { |
| "num": null, |
| "uris": null, |
| "text": "not t o o complex. The system's domain o f discourse is TIC-TAC-TOE: it. plays a game and discusses the game in progress. It has a ( c u r r e n t l y limited) s e t o f semantic concepts, and attempts t o interpret the u s e r ' s sentences ih terms of these. The set of semantic concepts is FIXED. The system does not attempt t o learn new concepts, merely new ways of describing concepts t h a t are already familiar. When t h e system encounters a new word or a new phrasing, it uses contextual information t o detername the p o s s i b l e meaning, and to ask clarifying questions which a r e as i n t e l l i g e n t as p o s s i b l e . It then remembers the meaning of the new e&pressfon, and w i l l use it i t s e l F *en it talks.", |
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| "text": "machine output by and comments added by the author arg in small type, enclosed in brackets, (HACHINE: I PLACE AM X IN TClE TOP LEFT SQUARE) [The machine makes its opening move. ] (USER: PLAY A ZERO IN THE CENTER WARE) (H: 1s ZERO A NOW HEANING 0 ) The new word *zeroa is added t o the machine's vocabu1ary.l (R: I PLACE AN X IN THE BOTTOM RIGHT WARE) (O: SLAP Ab4 0 IN THE BOTTOV! LEFT CORNER) (PI: IS (TO SLAP SOMETHING SOREWHERE) A WAY OF SAYING ( 7 0 PLACE SOMEmING SOMEWHERE)) (U: YES) (19: OK) [ A new surface framer ( a s described in section 4 ) I s added to the s e t sf surface b r a e s which express the concept of making a move.] (m: 1 SLAP AN X IN TWE TOP RIGHT SQUARE) f n t h i s sample s a s s i o n , the machine uses i t s newly acquired vocabulary a t every opportunity.) HOU CORE YOU PLACED AN X IN THE TOP RIGHT SQUARE) (H: IS (HOW CQHE MMEONE DOES SOHETHING) AN EXPRESSION ISEANIMG ( surface frme i s added t o those sxpssssing the concept ' I Syntactic featuras are associated w i t h each such frauge i n d i c a t i n g such things as Wether subject-verb invsrsion is ds be DerBomed.1 (U: CAN YOU CURRENTLY BEAT M E ) m: 1s CURRENTLY AN ADVERB REFERRING TO THE TInE OF A RWE) [These are the only adverbs the system knows a b o u t . In f a c t , a t present, the system ignores adverbs in its sewantic processing. I % asks the question because it wants t o account for all t h e words in the sentence. Notice that two top squares contain an x. However, only the l e f t onel was played while the bottom right square was Free. Therefore the systcszn concluded t h a t the top l e f t square was being described. This example illustrates how contextual information can be used t o ask as intelligent a cBarifying question as p a s s i b l a . ] (U: *yOW COME YOU PLACED AJ4 X IN THE TOP EAST SQUARE) ( ! I : IS EAST AN l@JECTLVE NEAWXNG) LAY SONETHING SOHEWHERE) A WAY OF SAYING (TO SLAP SOflETHING IS (TO POLISH SOMEONE OFF) A WAY OF SAYING) E I THER ( 1 : ( TO BEAT SOMEONE) ) (2: (TO LOSE TO SOMEONE)) (TO TIE SOMEONE)) (U: I ) (?' \u00b6: OK) (R: BY LAYING AN X ON WE TOP CENTER CELL) [ I f t h i s same session were subsequently repeated, sf course, the system would n o t need t o ask any questions of t h e ushJr. 1 Comments on C u r r e n t Lirnitatians There are a number of l i m r t a t i o n s to the a d a p t i v e system as it now s t a n d s . Some of these may be apparent in the smple session, bud an i n t r o d u c t i a n t o the system is not complete without d i s c u s s i n g them explicitly.", |
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| "text": "shows ths various stages that the Adaptive System gees through in understanding a sentence. In this sectian, we s h a l l watch while t h e system processes the sentence \"Mow came you placed an x in the top right ~q u a r e .~ ( 1 ) Local S y n t a c t i c Processing: In this f i r s t stage, the system scans the entire sentence l o o k i n g f o r l o c a l c o n s t i t u e n t s . These i n c l u d e Hsimplem noun phrases (NPs) and prepositional phrases (PPs), (\"simplen meaning 'up to the head noun but not including any modifying clauses or phrases\"), and verb groups (VGs) consisting o f verbs together with any adjoining rnodals, auxilliaries, and adverbs. In t h i s instance, t h e system Finds the t w o N P s , \"youe and \"an x m , the PP \"in the top r i g h t squarem, and the VG nplacedw.", |
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| "text": "Semantic Clustering: A t t h i s s t a g e , the c l a u s e -l e v e l processing s t a r t s . U n l i k e most systems, this c l a u s e -l e v e l processing is driven by SEMANTIC r s l a t i o n s h i g s , rath-er than by syntactic form. It uses a semantics-first kclustssinsg*, with a sscondary use of syntax for cormnents and confirmation+ In t h i s example, a l l t h e l o c a l constituents found can be clustered i n t o s description of e single concept: t h a t o f making a nave, Section 4 describes the mechanics of this stage in more detail. Cluster Expansion and Connection: During t h i s stage an attempt I s mada t o account Psr each word in t h e sentence by expanding the concept c l u s t e r s , and i f there i s more thaw one, by j o i n i n g them together t o form an e n t i r e multicXausa1 sentence-In t h i s case, ths concept c l u s t e r rnlght b s axpanded I n two ways. a ) One possiblllty n i g h t be t h a t I t i s a \"MOW\" type q u e s t i o n , and t h a t wcornc.tn is some sort of adverb, However this possibility v i o l a t s f a semantic constraiet, since the system is not s e t up t o answer haw a move is made; only how t o win, how t o prevent sorneons From winning, e t c .T h e r e f o r e t h i s possibility is ignored. b) The other p o s s i b i l i t y f r; t h a t \"how come\" i s a new way o f d e s c r i b i n g soma other clause f u n e t t o n . Contextual Inference; Clarification; and Response: During t h i s f i n a l staga, any c o n t e x t u a l inf~rrnatfsn a v a i l a b l e is broughtt o bear on araas of uncertainty, any necessary clarifying questions a r e asked, and the system responds t o the sentencs. In this example, t h e only uncertainty is the meaning of \"how comew. Since t h i s i s the main Adaptive System Overview clause of the sentence, the possibility of its b e i n g an Wn or *aftsra clause are discarded. The remaining p o s s i b i l i t i e s are n i m p e r a t i v s w , \"hown, m~h y n , and \"canw. The system does n o t answer %own and \"canw quest ions i n relation t o making moves. Similarly, \"imperativen does n o t make sense since the action described i s a previously made move. Therefore the system asks i f \"How come someone does somethingw means V h y does someone do somethingn. The user answers \"yesn, s o the system stores t h i s new way of asking \"whyn, and proceeds t o answer the question.", |
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| "text": "a t the clause level.In a t o p -d o m system, a sentence must exactly match t h e b u i l t -i n syntax before semantics can even be called and given the various c o n s t i t u e n t s o f a clause, T h i s IS clearly undesirable when one i s dealing with i n p u t uncertainty, since one cannot be sure exactly how t h e user will phrase his sentence. One would prefer to Bet semantics opera%@ First on any local consituents present, so that i t can make a reasonable grgss as to what is being discussed.Assemantically-rslated clusters of local constf tuents are found, syntax can be consulted and asked to comment. on the rslative g r m m a t i c a l i t y of the various c l u s t e r s . If there are two competing semantlc inte~pretations of one part of a sentence, and syntax l i k e s one much better than the other, then the \"syntactically pleasing\" interpretation can be pursued f i r s t . Later, i f this does not pan out,the syntactically irregular possibility can be looked at as wsP1. In t h i s way, syntax can help guide the system, but is not placed in atotally controlling p o s i t i o n . A by-product advantage o f t h i s s e m a n t i c s -f i r s t approach i s that the system can handle mildly ungrammatical input without any e x t r a work, In a d d i t i o n , t h e semantics-first c l u s t a r i n g approach lends i t s e l f q u i t e naturally t o handling sentence fragments.I n the remainder of t h k s s e c t i o n , we describe how the adaptive system organizes i d s linguistic knowledge t o implement this semanticsf i r s t approach. As we s h a l l s e e , there are three componeflts o f this knowledge. ( a ) Ths local racognizars which initially find local constituents. recognizers are represented t n Augmented Transition Network [ I l l f o m , are q u i t s s i m p l e , and are not described further i n t h i s p a p e r . (b) Clause-level knowledge sf how actions and clause-functions are described. This knowledge is expressed i n a descriptiva f a s h i o n which makes it msily manipulabla, and easy to add to. ( c ) Clause-level s y n t a c t i c knowladge which is sxprssred i r a a domainindebpendent fom.", |
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| "text": "l l u s t r a t e s how t h e system s t o r e s i t s knowledge sf how a c t i o n s ( o r events) are described. This knowledge is stored a t two l e v e l s : the conceptual l e v e l , and t h e surface (or expressive) l e v e l As shown in F i g . 2, the concept PLACE represents the a c t o f making a TIC-TAC-TOE wove. ( a ) On the CONCEPTUAL l e v e l , there are three \"conceptual s l o t s ' i n d i c a t i n g t h e actors which are involved in the a c t l o n : a player, a @ark, and a square. (b) On the SURFACE, or expressive, level there is a list sf surface frames each indicating one p o s s i b l e way t h a t t h e concept can be expressed. Each surface frame conslsts o f a verb p l u s a set of s y n t a c t i s case frames to be f i l l e d by t h e a c t o r s . (Notice that neither the conceptual slots nar the s u r f a c e frames i n d i c a t e explicitly t h e order in which the varlous constituents are to appear Fw a sentence.) When the system processes a sentence, it fills t h e c o n c s p t u a l shots w i t h local constituents found rn the sentence I f i t h a s f o u n d a f m i l i a r verb, then i t a l s o g e t s any surface e ( s ) associated w i t h that verb. A t this p o i n t i t c a l l s syntax, asking for c s m e n t s . For instance, i f the input sentence is \" 1 place an x in the corner\", t h e n all the conceptual slots of #PLACE would be f i l l e d , and the system would pass the following string to syntax wagen% verb o b j ppw . As a result, clause-level syntax does not see t h e a c t u a l constituents of the sentence, only t h e l a b e l s specifled I n the surface case frame, plus information indicating number, tense, etc .", |
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| "text": "I f the verb and constituents a r e familiar: one of Ghe conceptual s l o t s , and any surface frames associated w i t h the verb can be examined The frame ~n d i c a t e s the c s s e (agent, object, etc. ) associated with each c o n s t i t u e n t whon that verb is used. The frame is used t a create a string of case l a b e l s t h a t a r e s e n t t o syntax for c o m e n t s .For instance, iF the sentence is \"1 place an x i n the center L i n g u i s t i c KnowleMge about Actions square\", the string passed to syntax is \"agent verb obj pp\". Syntax", |
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| "text": "the, word ~c r o s s u is unfamiliar, Here, during t h e semantic clustering, t h e conceptual s l o t s for a player and a square can bs f i l l e d by \"Iu and \"in the center square\", b u t the slot for a mark is u n f i l l e d . I n a d d i t i q , there is the unknown constituent \"a crossg. A natural hypothesis, therefore, is t h a t the unknown constituent refers t o a type of mark. Since the verb is familia~, a surface f r m e i s avaflable. Next, assumtag the unknown constituent is a mark, the s t r i n g \"agent verb o b j ppw can be passed to syntax. Men syntax approves, this offers a d d i t i o n a l confirmation t h a t the hypothesis is probably r i g h t . Subsequent evaluation of this hypothesis indicates t h a t the sentence makes sense only if the mark referred to is Et n x , so the system asks i f \"crossu is a noun meaning the analysis. Instead, syntax must ba used in a different mode t o propose what t h e surface frame should be. Suppose the sentence is \"I plunk an x in the center squareM. Here, a l l the constituants can be clustered into the concept #PLACE, but t b r e is an unknown word, and no verb. Ths loglcrrl hypothasis is t h a t t h e new word i s a verb. A special syntactic module i s therefore passad t h e followfag s t r i n g \"NP(P) verb(p1unk) NP(M) PP(in,S)# This module examines the string and produces tn new Frame:", |
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| "text": "Since the system uses the surface frames to generate its o m replies, it can now-use this new frame i t s e l f when it talks. When the system wants to generate a c l a u s e , it passes a selected frame, the constituents, and a list of syntactic features to a clause generator which o u t p u t s the specified form. (Thus, c l a u s s -l e v e l syntax can be used by the system i n three different modes: (1) to comment on the g r m a t i c a l i t y of a s t r i n g of case markers, (2) t o constrbct a new surface frame, and ( 3 ) t o generate clauscas when tha system itself replies. ) 4.2 Knowledge of' how Clause-Functions are Described As i l l u s t r a t e d i n Fig. 3, knowledge of how clause-function concepts are described i s also expressed as two Lexals. Why does someone do somsthkng\") flow come ACTION() ( a s i n :\"Now come someone does something\") Linguistic can be expressed. A clause-type frame currently includes any special words which introduce the c l a u s e ( i e . \"whyn or \"how comen), together w i t h a list sf syntactic proparties which should be present in the clauss. This list of syntactic properties might include SVIMV, nsubjec$-verb inversionw (as in \"why does someone do something\"), ar 9 u b ject deletionH, 'ING f o m m , and \"use of a particular preposition* ( a s i n \"from doing somethingw).", |
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| "text": "Using the Clause Function Knowledge In this section we examine how this clause function knowledge can be used. With no uncertainty: I f the i n p u t sentence is \"Why d l d you place an x in the center squarew, then during the semantic clustering the string Rdo agent verb obj ppu i s passed t o syntax, which replies t h a t subject-verb inversion has taken place. When exarninlng t h e whole clause, the system sees t h a t it e x a c t l y matches one of the surface frames for a #WHY-type question, since it starts with the word n~h y V i n d contams subject-verb inverslbon, Suppose, however, the sentence had been \"Why you place an x IR the center squaren, or \"How come d i d you place an x i n the center square*. Each o f these sentences matches a surface frame for a MY-type question, except that i n both cases subject-verb inversion i s incorrect. In such a case, the system can, if it chooses, decide t h a t the user has made a minor error, and allow the sentence t o be processed anway. The locally-driven semantics-first approach Lets this happen i n a natural way. ( 2 ) A new surface frame: Another problem arises when a new clause introducer is encountered, as i n : \"Wherefore d i d you place an x i n the center squareM. Here, as described i n section 3 , the system hypothesizes that this may be a new way of asking a #WHY-type question. Since syntax reports that subject-verb inversion has taken place, the system can therefore create a new surface frame: Wherefore ACTIOM(SV1NV) t o be added t o the frames associated w i t h #WHY. In summary, the adaptive -5ys tern stores i t s l i n g u i s t i c knowledge i n a very accessible form. I t is not embedded in the parsing l o g i c . howledge of how actions and clause-functions are described is represented i n a descriptive, manipulable format. Syntax is domain independent, and is used only t o make cornants, with semantics playing the guiding role. This organization allows the parsinglunderstanding process t o proceed kn a flexible fashion, Language communication is an i n h e r e n t l y a d a p t i v e medium. One sees t h i s c l e a r l y ~f one t a k e s a problem t o a lawyer and spends time trying t o assimilate t h e r e l a t e d \" l e g a l e s e n . One a l s o sees i t i n any conversation where a persron is t r y i n g t o convey a complicated i d e a , expressed i n his own mental terms, t o someone else. The l i s t e n e r must t r y t o r e l a t e t h e words he Rears to h i s own set of concepts. Language h a s , presumably, evolved t o f a c i l i t a t e t h i s s o r t of i n t e r a c t i o n .Therefore i t is reasonable t o expect t h a t a good deal of the structure of language is i n some s e n s e s e t u p t o assist i n this a d a p t i v e process. By t h e same t o k e n , studying language from an a d a p t i v e standpoint s h o u l d p r o v i d e a f r e s h p e r s p e c t i v e on how t h e v a r i o u s levsls of l i n g u i s t i c structure i n t e r a c t .", |
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