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| "date_generated": "2023-01-19T02:40:37.075730Z" |
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| "title": "Watson Research Center, Yorktown Heights, New York, 3 972", |
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| "abstract": "Ihe maohine translation problem has recently been replaced by much narrower goals and computer processing of language has become part df artificial intelligence (AI), speech recognition, and structural pattern recognition. These are each specialized computer science research fields with distinct objectives and assumptions. The narrower goals involve making it possible for a computer user to employ a near natural-language mode for problem-salving, information retrieval, and other applications. Naturdl computer responses have also been created and a special term, \"understanding\", has been used to describe the resulting computek-human dialogues. Phe purpose of this paper is to survey these recent developments to make the A1 literature accessible to researchers mainly interested in computation on written text or spoken language. 2. Weizenbam, J. , \"ELIZA-A Computer Program f o r t h e Study of Nstural", |
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| "paper_id": "J75-2011", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "Ihe maohine translation problem has recently been replaced by much narrower goals and computer processing of language has become part df artificial intelligence (AI), speech recognition, and structural pattern recognition. These are each specialized computer science research fields with distinct objectives and assumptions. The narrower goals involve making it possible for a computer user to employ a near natural-language mode for problem-salving, information retrieval, and other applications. Naturdl computer responses have also been created and a special term, \"understanding\", has been used to describe the resulting computek-human dialogues. Phe purpose of this paper is to survey these recent developments to make the A1 literature accessible to researchers mainly interested in computation on written text or spoken language. 2. Weizenbam, J. , \"ELIZA-A Computer Program f o r t h e Study of Nstural", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "The computer literature discussed in this paper uses several linguistic terms in special ways, when there is a possibility sf congusion, quotation marks will be used to identify technical terms in computer science. The term \"understanding\" is frequently used as a synonym for \"the addition of logical relationships or semantics to syntactic processing\". This use is substantially qarrower than the word's implicit association with \"human behavior implemented by computer'' the narrower use is introduced as a neutral reference point, The question of whether a computer porgram can operate in a human-like way is central to artificial intelligence. \"Do current 'understanding' program systems show how extended human-like capability can be implemented using computers?\" is a related pragmatic questton Initially this investigation sought to examine whether programs which \"understand\" language in the stipulated narrow sense are protatypes which could lead to expanded capability. Unfortunately, \"language understanding\" and its special subtopic \"speeeh understanding'' are insufficiently developed to permit profitable discussion of the original question Hence an operational approach to the recent literature is taken here. This paper outlines how \"language understanding\" research has evolved and identifies key elements of program organization used to achieve limited computer \"understanding\".", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "1, INTRODUCTION", |
| "sec_num": null |
| }, |
| { |
| "text": "Current A 1 programs for lankuage processing are organized by level and restricted to specified domains. This section presents those ideas and comments on the limitations that they entail.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "2, LEVEL AND DOMAIN", |
| "sec_num": null |
| }, |
| { |
| "text": "Three principal levels of language-processing software are 1. \"Lexical\" (allowed vocabulary) 2. \"Syntactic\" (allowed phrases or sentences) 3 \"Semantic\" (allowed meanings) ln practice all these levels must operate many times for the computer to interpret even a small portion, say two words, of restricted natural-language input. Programs that perform operations on each level are, respectively, 1. Word in a table? 2. Word string acceptable grammatically?", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "2, LEVEL AND DOMAIN", |
| "sec_num": null |
| }, |
| { |
| "text": "A program to detect \"meaning\" (logical consequences of word interpretations) must also perform grammatical operations for certain words to determine a part of speech (noun, verb, adjective, etc.)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Word string acceptable logically?", |
| "sec_num": "3." |
| }, |
| { |
| "text": "One method makes a tentative assigrlment, parses, then tests for plausibility via consistency with known facts.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Word string acceptable logically?", |
| "sec_num": "3." |
| }, |
| { |
| "text": "To reduce the complexity of this task, the designer limits the subset of language allower or the \"world\" (i.e. the subject)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Word string acceptable logically?", |
| "sec_num": "3." |
| }, |
| { |
| "text": "discussed. The word \"domain\" sums up this concept, other terms for \"restricted domain\" are \"limited scope of discourse\", \"narrow problem domain\", and \"restricted English framework\"", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Word string acceptable logically?", |
| "sec_num": "3." |
| }, |
| { |
| "text": "The limitation of vocabulary or context constrains the lexicon and semantics of the \"language\". The trend i n t h e design of software for \"natural-language understanding\" is to deal with (a) a specialized vocabulary, and (b) a particular context or set of allowed interpretations in order to reduce processing time. Although computing results for several highly specialized problems Le,g. 7, 231 are impressive examples of language processing in restricted domains, they do not answer several key concerns.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Word string acceptable logically?", |
| "sec_num": "3." |
| }, |
| { |
| "text": "2 . Are current \"understanding\" programs, organized by level and using domain reatrictidn, extendable to true natural language?", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ". Do specialized vocabularies have sufficient complexity to warrant comparison with true natural language?", |
| "sec_num": "1" |
| }, |
| { |
| "text": "The realities are severe. Syntactic processing is interdependent with meaning and involves the allowed logical relationships among words %n the lexicon. Most natural-language software is highly developed at the \"syntactic\" level Howwer, the number of times the \"syntactic\" level must be ent'ered can grow explosively as the \"naturalness\" of the language to be processed increases. Success on artificial domains cannot imply a great deal about processing truly natural language.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ". Do specialized vocabularies have sufficient complexity to warrant comparison with true natural language?", |
| "sec_num": "1" |
| }, |
| { |
| "text": "The systems cited in this section answer questions, perform commands, or conduct dialogues.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ", PROGRAM SYSTEMS", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Programs that enable a user to execute a task via computer in an on-line mode are generally called \"interactive\" Some systems are so rich in their language-processing capability that they are called \"conversational\" there provides a general discussion of \"semantic information and computer programs involving \"semantics\"", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ", PROGRAM SYSTEMS", |
| "sec_num": "3" |
| }, |
| { |
| "text": "The \"question-answering\" program systems described in The \"blocks-world\" system described in [71 contrasts with these in that it has sophisticated language-processing capability It infers antecedents of pronouns and resolves ambiguities in input word strings regarding blocks on a table.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ", PROGRAM SYSTEMS", |
| "sec_num": "3" |
| }, |
| { |
| "text": "The distinction between \"interactive\", \"conversational\", and \"question-answering\" is less important when the blocks-world is the. domain. The computer-science contribution is a program to interaet ,wfth the domain as if it could \"underktand\" the input, in the sense that it takes the proper action even when the input is somewhat ambiguous. To resolve ambiguities the program refers to existing relationships among the blocks.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ", PROGRAM SYSTEMS", |
| "sec_num": "3" |
| }, |
| { |
| "text": "The effect of [71 was to provide a sophisticated example of computer \"understanding\" which led to attempts to apply similar principLes to speech inputs. (More detail on parallel developments in speech processing is presented later.)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ", PROGRAM SYSTEMS", |
| "sec_num": "3" |
| }, |
| { |
| "text": "The early \"language-understanding\" systems, BASEBALL The former could \"handle time questions\" and used a bottom-up analysis method which allowed questions to be nested. For example, the question \"Who is the commander of the battalion at Fort Fubar?\" was handled by first internally answering the questian \"What battalion is at Fort Fubar?\" The answer was then substituted directly into the original question to make ic \"Who is the commander of the 69th battalion?\" which the system then answered. reports some second thoughts).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ", PROGRAM SYSTEMS", |
| "sec_num": "3" |
| }, |
| { |
| "text": "The work of many other groups could be added to this ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": ", PROGRAM SYSTEMS", |
| "sec_num": "3" |
| }, |
| { |
| "text": "In all of the program systems described thus far, In most \"understanding\" programs, information on a primi tive level of processing can be inaccurate; for example, the identification of a sound string \"blew\" can be inaccurately \"blue\" Subsequent processing levels combine identified primitives. If parts of speech are concerned, the level is syntactic; if meaning is involved, \"semantic\"; if domain is involved, the lave1 is that of the \"world\". Sach level can be an aid in a deductive process, leading to \"understanding\" an input segment of language. Programs NOW EXIST which operationally satisfy most of the following points concerning \"understanding\" in narrow domains (emphasis has been added)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "4, DEDUCTION", |
| "sec_num": null |
| }, |
| { |
| "text": "Perhaps tha most importaht criterion for undersvanding a language is the ability TO RELATE THE INFORMA-TION CONTAINED in a sentence TO KNOWLEDGE PREVIOUSLY ACQUIRED.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "4, DEDUCTION", |
| "sec_num": null |
| }, |
| { |
| "text": "MAY BE FJTTED . . . The memory structure in these programs may be regarded as 3emantic, cognitive, or conceptual structures.,.these programs can make statements or answer questions based not only an the individual statemegts they were previausly t o l d , but also On THOSE INTERRELATIONSHIPS BETWEEN CONCEPTS that were built up from separate sentences as information was incorporated into the structure . . .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "This I M P L I E S HAVING SOME K I N D OF MEMORY STRUCTURE I N WHICH THE INTERRELATIONSHIPS O F VARIOUS P I E C E S O F KNOWLEDGE ARE STORED AND I N T O WHICH NEW INFORMATION", |
| "sec_num": null |
| }, |
| { |
| "text": "[ 2 8 p p .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "THE MEANINGS O F THE TERMS STORED I N MEMORY ARE PRE-C I S E L Y THE T O T A L I T Y O F THE R E L A T I O N S H I P S THEY HAVE WITH OTHER TERMS IN THE MEMORY.", |
| "sec_num": null |
| }, |
| { |
| "text": "This has been accomplished through clever (and lengthy) computer programming, and by taking advantage of structure inherent in special proklem domains such as stacking blocks on a When such a system is used a user might f a i l to get a fact or relataonship because the natural-language subset chosen to represent his question was too righ--i.e., it includes a complex set of logical relationships not in the computer. Thos a block could result in a human-computer dialogue if the program has no logical connection between \"garage\" and \"car\" but only between \"garage\" and \"house\" (the program replies \"OK\" or \"??'!I1 to user input sentences)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "-4 )", |
| "sec_num": "3" |
| }, |
| { |
| "text": "I L I K E CHEVROLETS.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "-4 )", |
| "sec_num": "3" |
| }, |
| { |
| "text": "? ? ?", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "O K I CAN GET TWO I N", |
| "sec_num": null |
| }, |
| { |
| "text": "The computer failed to \"understand\" that there was no change of discourse subject. This is an example of a \"semantfo\" failure w h i~h could be overcome by interaction. That is; the human user would need to dnput one more meaning or association of a valid word so that computer \"understanding\" may be . Furthermore, this increase in time is added onto that which occurs when the size of lexicon is expanded. ks words are added, the number of trees that can Be-produced by the grammar's rewriting rules in an attempt to \"recognize\" a string expqnds rapidly. Hence in speech as in text processing, \"under,standihgn1 exists via computer yet it is not likely to lead to rhachine processing of truly natural language. Indeed the artificiality of speech \"understanding\" by computer is even greater than that of text input. The \"moon rocks where language, and probably spoken-language, \"understanding\" will be exhibtred. These developments will occur through careful design of tasks and use of advances in computer technology However, the general problem of machine' \"understanding\" of natural language--whether text Dr speech-is not likely to be aided by these developments.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "O K I CAN GET TWO I N", |
| "sec_num": null |
| }, |
| { |
| "text": "A large body of research in computer science is devoted to language processing. ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CONCLUS IONS", |
| "sec_num": "7" |
| }, |
| { |
| "text": "To enable \"intelligent\" processing by the computer (\"hrtificial intelligence\") 2. To produce a more useful way ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "1", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "f?", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "wpods, d. A., Avr Expenimazltcr-e P a m h~g 3 t~h . t~ d a t", |
| "sec_num": "34." |
| } |
| ], |
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| "raw_text": "W, A. Len, M. F. Xedress, and T. E. Skinner, \"A Prosoclically Guided Speech Understanding Strategy, \" TEEE 7mi~acCicrnb or: ,4l\\c0~4,ttcd, Spccot'r mid S i g r d P f~o c e~d i g (S$eccrL2 TAA~CQ. air IEEE Sylj304 LLUII OH S;~CCCJ~ Rcco_onLtio~l), Vot. ASSF-23, pp. JO233, Pebrtlg-lry 1375, Limited Vocabulary Recogfdtion Systems,\"", |
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| "text": "51 were sophisticated mainly in methods of solving a problem or determining a response to a statement. Other systems have emphasized the retrieval of facts encoded in English." |
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| "text": "\"language understanding\" depends on the \"deductive capabilities\" of the *Some experiments on problem-solving effectiveness of special programing languages in another context appear in [ 2 2 ] .program, t h a t i s , i t s a b i l i t y t o \"infer\" f a c t s and rela.tionships from given statements. In some cases deduction involves d i scerning s t r u c t u r e i n a s e t of f a c t s and r e l a t i m s h i p s . This s e c t i o n describes how \"imderstanding\" prOgrAmS qhemselves a r e s t r u c t u r e d and how t h a t s t r u c t u r e l i m i t s tfheir c a p a b i l i t y f o r general deduction. Theorem-proving programs use an inference r u l e i l l u st r a t e d i n [ 2 3 p . 611 t o deduce new knowledge. A formal succession of 1ogi.cal s t e p s c a l l e d r e s o l u t i o n s leads t o the new f a c t . The example t h e r e begins with P1 -P4 given: P1 i f x i s p a r t of v , and i f v i s p a r t of y , then x i s p a r t of y; P2 a f i n g e r i s p a r t of a hand; P3 a hand i s p a r t of an arm; P4 an arm i s p a r t of a man A pr'oof t h a t P9 a f i n g e r i s p a r t of a m a n i s derived by s t e p s , such a s combining P1 and P 2 to g e t P6 if a hand i s p a r t of y , then a f i n g e r i s p a r t of y Unfortunately, i t i s easy t o move o u t s i d e the domain where the computer can make u s e f u l deductions, and the formal resol u t i o n process i s extremely lengthy and thus p r o h i b i t i v e l y c o s t l y i n computer t i m e . In [31, 321 i t i s shown t h a t some statements (\"whol d i d not write ---?It) are unanswerable and t h a t t h e r e is no algorithm which can d e t e c t whether a question stated i n a zero-one l o g i c a l form can beb answered. Henc.etheorem proving is not: e-sential to \"deduction\" and \"understanding\" systems, natural or artificial, must rely on other techniques, e . g . , outside information such as knowledge aboutLhe domain." |
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| "text": "67% without use of semantic bowledge c . 44% without use of sptactic or semantic knowledge These results were obtained in October 1973, and have been improved since [501. However, a key limitation of this form of computer speech \"understandkng\" is response rate. Reddy estimated that the third w~rd-accuracy figure (without use of syntactic and semantic knowledge) would have to be in excess of 90% to allow the program to achieve a near-human response speed. The nature of computer \"understanding\" programs leads to problems of combinatoric explosion in number of alternatives and this lessens the usefulness of multilevel program organization (acoustic-phonetic, lexical, syntactic, semantic, domain, and user interactions) as much in speech processing as in text pro.cessing. Prototype speech \"understanding\" systems have been build 1 4 9 , 501 and newer acoustic-phonetic and syntactic techniques have been incorporated into this work [ 4 9 , 51, 521, yet it seems clear that the development of theory in prosody and grammar cannot provide a breakthrough to escape the combinatoric explosion. The reason is that the search of parse rrees and the use of semantics (look up related words)depend on a single context--both take geometrica'lly increasing amounts of computing time as the number of contexts grows." |
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| "text": "t o access d a a and solve problems (\"man-machine interaction\") Techniques in artificial intelligence and speech recognition have been developed to the extent that prototype computer program systems which exhibit \"understanding\" have been developed for highly limited conrexts. To extend these programs to larger subsets of natural language poses problens, it is unlikely that any of the yesearch directivns currently being explored will of thewselves \"solve\" the I1na.tural lan guage problem\". (The techniques include, but are not limited to, further developments in artificial intelligence programming languages [17, 18 20, 21, 551.; refinements in theories of grammar; improved deductive ability, possibly by better theorem-proving techniques; and the introduction of stressrelated features in the ehcoding of speech [52]. A useful collection of language models appears in [ 5 6 ] . ) Nevertheless, prorotype systems for \"understanding\" both text and speech are useful achievements of engineering, and spoken entry of data by humans to computers is beginning to be established by isolated-word re-cognizers which use a minicomputer dedicated to the task. A multiplicity of purposes beyond this simple but practical task of data entry are mentioned briefly in the foregoing discussion of \"interaction\". Developments along, another view of the evolution of that p r o c e s s , see [ 5 7 ] ." |
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| "content": "<table><tr><td>[ 9 ] ,</td></tr><tr><td>ELLZA, and STUDENT, were based on two special formats: one to</td></tr><tr><td>represent the knowledge they store and one to find meaning in</td></tr><tr><td>the English input. They discard all input information which</td></tr><tr><td>cannot be transformed for internal storage. The comparison</td></tr><tr><td>of ELIZA and STUDENT in [I] is with regard to the degree of</td></tr><tr><td>\"understanding\" ELIZA responds either by transfoiming the</td></tr><tr><td>input sentence (essentially mimicry) following isolation of a</td></tr><tr><td>key word or by using a prestored content-free remark. STUDENT</td></tr><tr><td>translates natural-language \"descriptions of algebraic equations,</td></tr><tr><td>... proceeds to identify the unknowns involved and the relation-</td></tr><tr><td>ships which hold between them, and (obtains and solves) a set</td></tr></table>", |
| "text": "with an ability to spout back similar to ELIZA's usually store a body of text and an indexing scheme to it. This approach has obvious limitations and was replaced by systems that use a formal representation to store limited logical concepts associated with the text. One of them is SIR, which can deduce set relationships anong objects described by natural language. SIR is designed to meet the requirement that \"in", |
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| "content": "<table><tr><td>forms of input questions</td><td>(Recent versions are described in</td></tr><tr><td>113-151 .)</td><td/></tr><tr><td colspan=\"2\">Deductive systems can be divided into general systems</td></tr><tr><td colspan=\"2\">which add a flrst-order predicate-calculus theorem-proving</td></tr><tr><td colspan=\"2\">capability to limited-loglc systems to improve the complexity</td></tr><tr><td colspan=\"2\">oE the facts they can \"infer\", and proccdurnl systcms which</td></tr><tr><td colspan=\"2\">enable other computations to obtain complex information The</td></tr><tr><td colspan=\"2\">theorem-proving capability is designed to work Erom a group</td></tr><tr><td colspan=\"2\">of logical statements given as input (or statements consistent</td></tr><tr><td>with the'se input s-tements)</td><td>However, facts</td></tr><tr><td/><td>[7, p. 371</td></tr></table>", |
| "text": "INCONSISTENT with the original statements cannot always be detected and deductive systems quickly become impractical as the number of input statements (elementary facts, axioms) becomes large [ b , 7, 161, since the time to obtain a proof grows to an impractical length. Special programming languages (e.g. QA4 [17, 181 , PLANNER [ 2 0 , 211 ) , have added strategy capabilities and better methods of problem representation to reduce computing time to practical values QA4 (seeks) to develop natural, intuitive representations of problems and problem-solving programs. (The user can) blend ... procedural and declarative information that includes explicit instructions, intuitive advice, and semantic definitions. \u20ac171 However, there is currently no body of evidence regarding the effectiveness of the programs written in this programming language or related ones on problem-solving tasks in general or \"lapguage understanding1' in particular. There is a need for experimental evaluation of the strategies that the progsahing language permits for \"language understanding\" problems. Procedural deductive systems facilitate the augmentation of an existing store of complex information. Usually systems require a new set of subprograms to deal with new data: each change in a subprogram may affect more of the other subprograms. The structure grows more awkward and difficult to generalize. . . . Finally, the. system may become too unwieldy for further expkrimentation. 15, p. 911 In procedural systems the software is somewhat modular In 19 \"semantic primitives\" were assumed to exist as LISP subroutines. PLANNER 1201 allows complex information to be expressed as procedures without requiring user involvement with the details of interaction anong procedures (but [21]", |
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| "content": "<table><tr><td colspan=\"2\">ing yields cumbersome processing, especially when there are</td></tr><tr><td colspan=\"2\">nested uncertainties. The recursive properties associated</td></tr><tr><td colspan=\"2\">with the data structure term \"list\" [271 are not easily</td></tr><tr><td colspan=\"2\">adapted to multiple meanings. Hence, representing linguistic</td></tr><tr><td colspan=\"2\">moving chess pieces, and retrieving facts about a data for computation is ah opwr a n d fundamental researrh</td></tr><tr><td colspan=\"2\">large naval organization. problem. Nevertheless, the programs which de~uce facts from</td></tr><tr><td colspan=\"2\">Program systems for understanding begin with a \"front language do so withnut a clear best technique for computer</td></tr><tr><td colspan=\"2\">end\": a portion designed to transform language input into a representation. To do this, restrictions on the language</td></tr><tr><td colspan=\"2\">computer representatiort. The representation may be as simple implicit in the input domain are used, and repeated process-</td></tr><tr><td colspan=\"2\">ing by level (lexical, syntactic, semantic) is used in the</td></tr><tr><td colspan=\"2\">absence of an efficient representation language. Data struc-</td></tr><tr><td colspan=\"2\">tures that facilitate following the language structure are</td></tr><tr><td>needed</td><td>Existing programs provide special solutions to the</td></tr><tr><td colspan=\"2\">The language problems of deductive processing in narrow language domains</td></tr><tr><td colspan=\"2\">processimg program DEACON used ring sqructures 1111, a repre-While these programs are not a general breaktht-ough in reure-</td></tr><tr><td colspan=\"2\">sentation frequently used to store queues. In principle a senting language data for computation, they demonstrate that</td></tr><tr><td colspan=\"2\">data structure can represent involved associations, but in current programming t.echniques enable a us.eful \"understanding\"</td></tr><tr><td colspan=\"2\">practice simple order or ancestor relationships predominate Completely different and far more complex types of structure are inherent in natural language. For example, from 1281 \"The-professors signed a petition.\" is not true. capability Furthermore, ticated human understanding capabilities</td></tr><tr><td colspan=\"2\">has for valid interpretations: 5 INTERACT I ON</td></tr><tr><td colspan=\"2\">Research and computer program development desrgned to</td></tr><tr><td colspan=\"2\">store multitudes of facts so that they can be accessed [ 2 9 ]</td></tr><tr><td/><td>petition:</td></tr></table>", |
| "text": "tbere is a reql potential for use ot the \"understanding\" in an interactive node to facilitate use of computers by nonspecialists and to tap fhe more sophisin linguistic form (see pp. 11-17 of [30]) is highly relevant to recent research programs in text and speech understanding.", |
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| "content": "<table><tr><td>[541</td></tr></table>", |
| "text": "A survey of the program systems that *Threshold Technology Inc. has sold such a system to s everal users. Their VIP-100 includes a miriicamputer dedicated to the recognition task; there are otker isolated-word systems", |
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