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| "institution": "University of Maryland", |
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| "first": "L", |
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| "abstract": "We describe a natural-language recognition system having both applied and theoretical relevance. A t the applications level, the prwram w i l l give a natural ccmmunications interface facility to users of existing i n t e r a c t i v e data management systems. A t the t h e o r e t i c a l l e v e l , our work shows t h a t the useful infoxmation i n a natural-language expression (its \"meaning\") can be obtained by an algorithm t h a t uses no formal description of synt-. The construction of the parsing tree is c o n t r o l l e d primarily by semantics i n the form of an abstraction of the nmicxo-world\" of the DMS's f u n c t i o n a l capabilities and the organizat~on and semantic relations of t h e data base content material. A prototype is c u r r e n t l y implemented in LTSP 1.5 on tho IBM 370/145 computsr at System Development Corporation. In a recent article in S c i e n t i f i c , American, Dr. Alphonse Chapanis says, \"Tf t r u l y interactive computer (; y s t m are ever to be created, they will ~omehow have to cope w i t h the... errors and v i o l a t i o n s of format t h a t a r e the rule rather than the exception in normal human ccmmunication\" [1]. A n example dialogue produced by t w a persons interacting w i t h each other by teletypewriter to solve a problem as~igned to them by experimenters showed that :not one grernaaatfcally correct sentence appears in t h e entire protocol. tl Many existing language pmcessors (woods, Kellogg , Thcmpson , e t c .) [ 2,3,4) a r e limited to what Chapanis calls \"Irmnaculate prose,\" that i s , \"the sentences that are fed into the computer are parsed in one way or another so that the m e a n i n g of the ensemble can be inferred frm conventional rules of syntax,\" which are a \u00a3 0-d e s c r i p t i o n of the language. In effect, users are required to i n t e r a c t w i t h these s y s t e m in s m e formal language, or at l e a s t i n a language that has a formal representation i n the computer system t h a t a user's expression must conform to (we are t h i n k i n g , in t h e latter instance, of Vhampsonls REL, which has an extensible formal representation facility). In a d d i t i o n , most natural-language question-answering systems, including all referenced above, require that a user's data be restruct-wedl and reorganized acwraing t o the p a r t i c u l a r data base requirements of the natural-language system to be used. A t the level of a r t i f i c i a l i n t e l l i g e n c e research [ti ,6 ,?'I , Mere is same interest in systems that recognize meaning i n natural-language expressions by methods that dd not m i r e compiler-like syntactic analysi~ of an expression prior to asmantic interpretation. We believe it is possible, practical, and feasible, using new lingufstic processing strategies, to design a natural-language interface system that will permit flexible, intuitive coaansmicatiba w i t h information management systems and other computer programs already in existence. This interface is open-ended in that it has Cozmnunications of the ACM, October 13, 1970, 3. Kellogg, C. H,, et a l , The CONVEXGE natural language data management system: current status and plans. ACM Sym~osium on Information Storaqe", |
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| "abstract": [ |
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| "text": "We describe a natural-language recognition system having both applied and theoretical relevance. A t the applications level, the prwram w i l l give a natural ccmmunications interface facility to users of existing i n t e r a c t i v e data management systems. A t the t h e o r e t i c a l l e v e l , our work shows t h a t the useful infoxmation i n a natural-language expression (its \"meaning\") can be obtained by an algorithm t h a t uses no formal description of synt-. The construction of the parsing tree is c o n t r o l l e d primarily by semantics i n the form of an abstraction of the nmicxo-world\" of the DMS's f u n c t i o n a l capabilities and the organizat~on and semantic relations of t h e data base content material. A prototype is c u r r e n t l y implemented in LTSP 1.5 on tho IBM 370/145 computsr at System Development Corporation. In a recent article in S c i e n t i f i c , American, Dr. Alphonse Chapanis says, \"Tf t r u l y interactive computer (; y s t m are ever to be created, they will ~omehow have to cope w i t h the... errors and v i o l a t i o n s of format t h a t a r e the rule rather than the exception in normal human ccmmunication\" [1]. A n example dialogue produced by t w a persons interacting w i t h each other by teletypewriter to solve a problem as~igned to them by experimenters showed that :not one grernaaatfcally correct sentence appears in t h e entire protocol. tl Many existing language pmcessors (woods, Kellogg , Thcmpson , e t c .) [ 2,3,4) a r e limited to what Chapanis calls \"Irmnaculate prose,\" that i s , \"the sentences that are fed into the computer are parsed in one way or another so that the m e a n i n g of the ensemble can be inferred frm conventional rules of syntax,\" which are a \u00a3 0-d e s c r i p t i o n of the language. In effect, users are required to i n t e r a c t w i t h these s y s t e m in s m e formal language, or at l e a s t i n a language that has a formal representation i n the computer system t h a t a user's expression must conform to (we are t h i n k i n g , in t h e latter instance, of Vhampsonls REL, which has an extensible formal representation facility). In a d d i t i o n , most natural-language question-answering systems, including all referenced above, require that a user's data be restruct-wedl and reorganized acwraing t o the p a r t i c u l a r data base requirements of the natural-language system to be used. A t the level of a r t i f i c i a l i n t e l l i g e n c e research [ti ,6 ,?'I , Mere is same interest in systems that recognize meaning i n natural-language expressions by methods that dd not m i r e compiler-like syntactic analysi~ of an expression prior to asmantic interpretation. We believe it is possible, practical, and feasible, using new lingufstic processing strategies, to design a natural-language interface system that will permit flexible, intuitive coaansmicatiba w i t h information management systems and other computer programs already in existence. This interface is open-ended in that it has Cozmnunications of the ACM, October 13, 1970, 3. Kellogg, C. H,, et a l , The CONVEXGE natural language data management system: current status and plans. ACM Sym~osium on Information Storaqe", |
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| "section": "Abstract", |
| "sec_num": null |
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| "text": "no prejudice about t h e user's system funckians and can be joined to almost any such system with relatively l i t t l e effort. I t i s , i n addition, able to infer t h e meaning of free-form English expressions, as they pertain to the host system, without requiring any formal description or representation of English.", |
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| "text": "The syntactic inflexibiiity of existing natural-language processors limits their usefulness i n interactive man-madine tasks. O u r approach does not use a collection of syntax rules or equations as they are normally defined.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "THE SEMANTIC INTEREACE ALTERNATIVE", |
| "sec_num": null |
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| "text": "Instead, we construct a dictionary in which w e define words in terms of their possible meanings with respect to the particular data base and data management system (DMS) we want to use and according to the possible relations t h a t can exist between data-base and I3MS elements ( e . g . , an averaging funct i o n on a group CKE numbers) i n the limited \"micro-world\" of this precisely organized data collection. Words appearing in a user's expression t h a t a r e not explicitly defined are ignored by the system i n processing the expression; an example would be t h e word \"the,\" which is usually not meaningful in a data management environment. Wa thus avoid the expressive rigidity that formal syntactic methods hposa on tha user and the excesaivcs time and resource consumption t h a t results from the catibinatorial explosions usually produced by such rnethade.", |
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| "section": "THE SEMANTIC INTEREACE ALTERNATIVE", |
| "sec_num": null |
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| "text": "We distinguish in their d e f i n i t i o n s beween two types of words: content words m d function w o r b (or \"operatore\"). Content words are w a d s whoae 'meaningsw are the objects, events, and concepts that make up the s u b j e c t s being referred t o by users, More p r e c i s e l y , for data axetnagernent systems, these meanings (or \"concepts\") are the f i e l d names and entz'y i d e n t i f i e r s f o r *e data b-e and the names for available IHS operations such as averaging, s d n g , sorting, comparing, etc. Function words serve as connectors of content words. Their use i n natural language i s to indicate khe manner in which neighboring conltent words ar'e intended to relate to one another. In the example \"the salary of the secretary ,\" used belaw, \"salary\" and \"secretary,\" are content words, and \"of\" is a function word used to connect theta. To get a more i n t u i t i v e understanding of this process, suppose, again, t h a t a data base contains e n t r i e s for both secretaries and clerks w i t h salaries fox each. Suppose \"Suzi&' is an instance of a secretary and om\" is an instance of a clerk. We then have three words defined as follms:", |
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| "section": "THE SEMANTIC INTEREACE ALTERNATIVE", |
| "sec_num": null |
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| { |
| "text": "Suzie ( (SUZIE SECY) ) Torn ( (TOM C-LK) ) Salary ( ( sECY SECSAL) (CLK CLKSAL) )", |
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| "section": "Many cmntent wor&", |
| "sec_num": null |
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| "text": "Processing me phrase \"Suzie ' s salary\" would i n t e r s e c t the Y i ( \" (SECY) \" ) from t h e d e f i n i t i o n of \"Suzie\" w i t h t h e Xi's (\"SECY\" and \"CLK\") from t h e definition of \"salary.\" The intersection is nan-empty (\"(SECY)\") , and, i n discovering the semantic relationship the sense \"SECSALI-' is assigned t o the word \"salary.\" Similarly, \"Tan's salary\" assigns t h e sense \"CLKSAL\" t o \"salary. !I", |
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| "section": "Many cmntent wor&", |
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| "text": "operates for a p a r t i c u l a r DMS/data-base t a r g e t system. It contains a particular &&ionc r e a t e d for t h a t t a r g e t system. For a p a r t i c u l a r dic- In the analysis of a particular input by our system, two words i n context a r e t e~t e d using t h e \"intersection\" method described abave and, if they are found to be semantically r e l a t e d , they are considered candidates f o r \"connection\" as descrrLbed below. Two words so connected \u00a3 o m a phrase. Eunction words may connect content words in \"positive,\" \"negative ,\" or \"peak\" connections. me follming are examples of each mannax of connection:", |
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| "section": "A particular bplmentation of the natural-language interface processor", |
| "sec_num": null |
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| "text": "1. \"Of\" is a negative operator, as in \" t h e salary of the SALARY 2.", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "A particular bplmentation of the natural-language interface processor", |
| "sec_num": null |
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| "text": "\" ' 8 \" is a positive operator, as in \"the s e c r e t a r y ' s salary\":", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "A particular bplmentation of the natural-language interface processor", |
| "sec_num": null |
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| "text": "3 . \"And\" is a peak operator, as in \"Atlantic and Pacific. ", |
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| "eq_spans": [], |
| "section": "A particular bplmentation of the natural-language interface processor", |
| "sec_num": null |
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| { |
| "text": ". Woods, W. A, Trahsition network gr-ars for natural language analysis.", |
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| "FIGREF0": { |
| "uris": null, |
| "type_str": "figure", |
| "text": "are context sensitive, In a particular data base, f o r btmcm, the ward \"salary\" may refer t o the data-base f i e l d name SECSAL if the saXW frs \"of a secretary,\" but may a l s o indicate the f i e l d name CLKSAL if it is a *salary of a clerk.\" In recpgnition of this we therefore d e f i n e eaah aontent word by a set of one or more pairs of the form ( ( X I Y l ) (X2 Y2) . . . (Xn Yn)) where the Xi a d Y i are \" o o n c e p~\" (that is, f i e l d names, etc.) as described above. This expression may be interpreted as, \"if the word so defined i r j t contactually related in a sehtance to Xl, its particular meaning in this centact is Y 1 , if it i s r eo related b X2, it meme Y 2 , m d ao forth.\" This particular oontextual mnaranfng af the word is callad its sense. Two c o n t e n t warm are consrid=& t o bls artmantically related i f the i n t e r s e c t i o n o f the X i ' a fmtn the definition of one wort! w i t h the Yi's from the d e f i n i t i o n of U1Q other i r a not empty.", |
| "num": null |
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| "FIGREF1": { |
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| "text": "tionary, the s e t o f a21 l i s t s 05 p a i r s as described above, therefore, c o n s t i t u t e s the equivalent of a ~a n c c p t q~a p h ox network for the p a r t i c u l a r data b a a malogous to those U R Q~ hy many of t h e more conventj-onall, parsers Pox semantic analysis folluwing (or during) the syntactic phase of parsing.", |
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| "FIGREF2": { |
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| "text": "words are defined as operators or processors t h a t perform this semantic t e s t . The d e f i n i t i o n of one function word d i f f e r s fm that of another according to its slope (see belaw) and also in t h a t t h e operational definition of a function word can reject a connection even though t h e two words may be samntically related. In the operational d e f i n i t i o n of t h e function word may be a list of acceptable concepts or a rejection list of unacceptable concepts. In most conceivable data bases, the phrase \"salary in the secretary\" would be thus rejected by t h e function word \"in. n As the analysis of an i n p u t expression proceeds, a \"clumpifig\" of word and phr as e meanings more and more explicitly normally, processing of the e n t i r e sentence r e s u l t s in a tree s t r u c t u r e made up of the connected senses of a l l the content words fran the sentence. This result we term the sentence qraph even though the input expression may not be a grammatically cmplete sentence. This sentence graph will be t r a n s l a t e d i n t o statement.We recognize t h a t the linear ordering of the words in an input expression is not entirely randm and t h a t certain aspects of me function of syntax must be taken into accorunt. This is done by means of a new and p w e r f u lazgorithm b k d on what we c a l l the syntactic-semantic slope. Linguists generally recognize that whenever two units of meaning are combined, one is semantically domfnant and t h e other subordinate, as a modifier is subordinate to the modified word. A f t e r coenbinatfon, t h e d d n a n t word may be wed in m o s t cases to refar to the canjoined pair. Thus, a \"red herring\" \"salary.\" If this relationship of dominance i s represented vertically on a ltrectangular graph (i.e., dominance on the Y-axis), and if t&e l i n e a r ordering of the words in the expression is represented on the X-axis in n o w 1 left---right: order, then the connection of an adjacent pair of content words or phrases will describe a linear slope on the graph. The slope is positive eir negative as the dominating sub-unit is, respectively, to t h e right or to the l e f t of the subordinate sub-unit. For example, the phrase \"red herring\" makes a positive slope, thus: ~p e r a~o n a l meanings of fqnctian words operate on the meanings of nearby content words. Dominance is assigned, semantic relationships are verified, and the relationships so discovered are accepted or rejected. If accepted, the two word-meanings are connected, and the acceptable sense is assigned to t h e dumllnant word.", |
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| "FIGREF3": { |
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| "text": "\" In contrast w i t h positive and negative operators, peak operators add a representation of their m semantics i n t o t h e structures t Between any two adjacent content words there is an implicit \"empty\" operator t h a t is a positive operator, as in \"red herring\": RED In general, all prepositions are defined as negative operators. This is equivalent Go the rule used by syntactic processors. The positive empty operator is equivalent while vexbe and conjunctions a r e defined as peak operators, giving our atatemcnt o f rules such errs s + N P v E ' N P MP + NP CONJ NP.Each operator has the f a c i l i t y to accept or r e j e c t any semantic rejlation accordin9 to the precise d e f i n i t i o n of the function word for the host data management system.Progressive connection of word meanings and previously connected groups or \"phrase meanings\" results in a tree graph t h a t we c a l l the sentence qraph.For example, the question \"What is ;t;he surface displacement of U S . diesel submarines?\" could, f o r a particular data base, produce from the dictionary a string of content-word and funeion-word definitions that might be represented typographically l i k e this:( (SUB SURE-DISC) ) <OF> ( (U . S. LOC) ( (DIESEL TYPE) ) ( (LOC SUBS) (TYPE SUBS)As a xesult of processing, these will assemble into a tree structured (using the senseg of the words) l i k e this:t r e e , o r sentence graph, i s created as a result o f semantic relationships instead of Eonnal r u l e s of grammar, it still. closely resembles the \"parse t r e e \" produced by m o~t conventional syntactic language processors.With respect t o the user's target data management system, t h e sentence graph is preci~e and unambiguous and contains enough information for a straightforward translation into the formal query language of the EMS. In SDCrs DS/3 lanwage, f o r example, the above question would be expressed as PRINT SURF-DISP WHERE TYPE EQ DIESEL AND lXXl EQ U.S. The response to the usex's question will thus be the response frclrn h i s DMS t o the formal query statement. The user's input in this hypothetical example i s proper i n fom and grammar. However, it need not have been. The request OBTAIN SURFACE DISP FOR US SUBS SUCH AS HAS TYPE EQ DIE=. would produce exactly the same sentence graph and thexefore, exactly t h e same f o m l query statement with the same response f r o m t h e DMS. It is not l i k e l y t h a t a syntax-based parser would have anticipated t h e odd laxxguage-use and grammar of this last request. Without a syntax rule t h a t would alluw for the phrase \"such as has\" such a parser would not look at the semantics involved and would be unable t o interpret the request. Our syntax algorithm gets t h e same results that would be expected f m m the application of syntax rules without the need t o a n t i c i p a t e each grammatical construct expected from the user. In overview, t h e parsing algorithm makes a series of positive, negative, and peak connections based on the operational meanings of t h e function wards (including the \"empty\" aperator) and on the relations between meanings of the content wort%?. The algoridt-Xlm adheres to the following rules: e Connections between content words are possible only if the result of the intez'sectfon t e s t described & m e is non-empty and i f this result i s not rejected by the operation of the function word p e r f o d n g t h e test. The function word d e f i n i t i o n also determ i n e s which w o r d supplies its X ' s and which its Y's for the t e s t , I t thus controls which w o r d has its sense d e t e d n e d if t h e t e s t ia successful. Most of ten (though there are exceptions) , p o s i t i v e operators use t h e X's f r o m t h e w o r d to the r i g h t and t h e Y ' s from the word to the left of . b e operator. P o s i t i v e operators, thesefore, determine the sense of the word t o t h e right. This is i l l u s t r a t e d using, again, t h e secretaxy and her salary, Consider the d e f i n i t i o n of \"Suzie\" and \"salary\" as shown on page 5 , The phrase \"Suzie's salazy\" has two content w o r d s , \"Suzie\" and \"salary, \" separated by the function word , \" s , \" This function word i s a positive operator and, hence, applies t h e intersection t e s t t o the X i from the definition of \"salary\" w i t h t h e Yi from the d e f i n i t i o n of \"~u z i e . \" These values are, xespactively, ' I (SECY CLK) \" and \" (km) . \" The i n t e r s e c t i o n y i e l d s \" (SECY) , \" which is acceptable to the \" ' s \" operator, and the connection is made with \"salary\" as the dominant word. The sense of \"salary\" is the Y i associated with \"SECY\" in t h e d e f i n i t i o n of \"salary,\" hence, \"SECSAL.\" T h i s selection process is reversed f o r negative aperators, while peak operators employ both kinds of t e s t s , one on each s i d e of t h e peak. Rule 2: N o node i n a sentence graph may have m o r e .than one dominating node. That is to say, a l l connections m u s t r e s u l t i n trees, This I s a canmon asswnptLon consistent with conventional syntax-driven parsers. Given a subtree, a c o n s t i t u e n t on its left has the p o s s i b i l i t y of conneation only to nodes of the subtree's positive adjacent slope, and a c o n s t i t u e n t on the r i g h t can connect onLy t o the nodes i n the adjacent negative slope. I n t u i t i v e l y , this means that if the nodes of a subtree are connected by \"lines\" that are \"opaque b a r i e r s r n then a constituent on either side of t h e s u b t r e e may connect to it only on those nodes that it can rlsee.r' I t may not connect t o nodes on the \"inside\" or the \"fax s i d e \" of the subtree. This i s a powerful h e u r i s t i c r u l e t h a t eliminates t h e need t o t r y connections to many syntactically impossible portions of t h e subtree. In effect this one rule, together w i t h the definitions of the function words, replaces all the syntax rules used by most conventional parsers. Rule 4: I n order t o minimize disconnection of existing subtree structures (badcup) and s t i l l consider a l l possible connections, the system should, whenever possible, constrztct,subtrees s t a r t i n g from t h e top and make new connections from belaw. This rule leads to the following algorithm: Scan the consUtuents from left t o right making negative connections, then scan from right to left making positive connections. S c a n thus back and forth u n t i l no more connections can be made. Then make any poasible peak aonnect i o n s and repeat the algorithm. Continue t h i s process u n t i l a l l c o n s t i t u e n t s have been connected i n t o a single tree, We have observed t h a t if ambiguities exist under these conditions, they w i l l be semantic and, in all probability. not resolvable by any further processing or analysis of the expression. Therefore. there is no need to carry along temporary multiple construction p o s s i b i l i t i e s , The algorithm may eirher query t h e user at this point for disambiguation or W d w t the pxocesging and", |
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