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| "abstract": "C o l b y , K A. , et al. ~rtifieial pasaboia. ~rtificial Intelligence 2 , (1 9 7 1) , 1-25. 9. Colby, K. M. , and arki ins on, L C. Pattern-matching rules for t h e recognition of natural Bangbage dialuogue expressions. A J C L Microfiche 5, 1 9 7 4. 8.", |
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| "text": "C o l b y , K A. , et al. ~rtifieial pasaboia. ~rtificial Intelligence 2 , (1 9 7 1) , 1-25. 9. Colby, K. M. , and arki ins on, L C. Pattern-matching rules for t h e recognition of natural Bangbage dialuogue expressions. A J C L Microfiche 5, 1 9 7 4. 8.", |
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| "text": "Microfiche 7 2 : 22 I N T 4 0 D U C T I O~ T O C O N T E M P O R A R Y L I N G U I S T I C S E M A N T I C S GEORGE L. D I L L O", |
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| "section": "American Journa of Computational Lihgubtics", |
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| "text": "t h e r e is a v a l u e of y, namely lo., t h a t makes t h e conditional in (3b) t r u e ) . The f a c t t h a t Dillon allowed formulas l i k e (3b) . 36 After an unsucc&sful attempt to construct puzzles by whole insertion of words, puzzles w m successfully constructed by a letter by letter method. Usuaily when a word was validly formed by the letter by letter puzzle constructor it could remain permanently in tbe constructed puzzle. A dynamic, heuristically determined, decision structure was required. The constructor resolved questions of letter selection, ordering and reordering of the solution sequence, dictionary structure%snd access, and decision path selection. Recent work in A1 suggests that intelligence is based on the ability to use large amounts of di~erse kinds of knowledge in procedural ways (e.g. frames, scripts), rather than on, the possession of a few general and uniform principles (e.g, heuristic swrch). Within this general framework ca fundamental contribution of A1 to epistemology is clear: the systematic introduction of aktive agents into epistemological theory construction so that items of knowledge are active agents. Other contributioals of A1 include concQts of system relfknowledge (the system's ability to observe its behavior, and to make use of those observations, even to the point of learning to debug faults in its procedures) and the development of a variety of control structures (e.g. ATNs). Finally, the paper considers ways in which A1 may have a radical impact on education if the principles which it utilizes to explore the representation and use of !tnowledge are made available to the student to us: in his owan learning experiences. The field of computational linguistics is part of the slowly unfolding-revolution in man's wy of thinking about thought itself, a revolution spurred and supported by the computer, As revolutionari@ we have to do without the guidelines that tradition furnishes others; but the luckiest scientists are those like ourselves who, being revolutionaries, have the best chance to make big contributions. The first experiment concerned the acoustic pr or (the mapper) and the second concern4", |
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| "section": "American Journa of Computational Lihgubtics", |
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| "text": "A R T I F I C I A L I N T E L L I G E h C E . 38 CONFERENCE-WORKSHDPS. . . 40 J O U R N A L , ,", |
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| "section": "American Journa of Computational Lihgubtics", |
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| "text": "'fanout'the number of alternatives at each word, both for the language alone and in combination with the acoustics. The third experiment studied the effects of four controlstrategy design choices. Focus by inhibition and island driving had bad effects, while context checks for priority setting hzd good effects. Mapping all at once had g o d effects on everything except acoustic and total runtime, and these bad effects could probably be eliminated by redesign of the mapper. 73s fourth experiment varied the size of allowed g a p and overlaps between words and showed the potential value of special acoustics tests to verify word-pair junctions. A fifth experiment concerned the effects d increased v~u b u l a r y and improved acoustic accuracy while a final study concerned detailed measurements of the Executive performance and provided insights into the use of time and storage and the kinds of errors made by thc system. N a t u r a l E a n g u a g ", |
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| "start": 929, |
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| "text": "N a t u r a l E a n g u a g", |
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| "section": "American Journa of Computational Lihgubtics", |
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| "text": "Charles 3, Fillmore A computer program for synthlsizing speech by rule from phonetic data has been modified so that the rules for generating stop consonants (/b, d, g, p, t, k/) A program has been designed and implemented in SIMULA 67 on a DECSystem-10 to play the SCRABBLE Crossword Came interactively against a human opponent. The hart of tbe dedgn Is the data sincture for the lexicon and the algorithm for searching it. The lexicon is represented as a letter table, or tree using canonical ordering of the letters in the words rather than the miginal spelling. The algorithm takes the tree and a collection of letters, inclading blanks, and in a single backtrack search of the tree finds all words that can be formed from any combination and permutations of the tetters. Words using the higher valued Ietters arc foi~nd before words not using those letters, apd words using a collection of letters are found before words using a sub-collaction of them. The Search procedufC detaches after each p u p of words is found and may be resumed if more words are desired. Decet~rber. 1976 Topics: Dictionary. Phonological rules, Dictionary expansions, Lexical retrieval, Control strategy, Performance. The Lexical Retrieval component determines the 11 most probable word matches in a full lexicon or appropriate subset and (operates on a phonetic segment lattice. Words can be matched lef t-to-right and right-to-left Control strategy options are governeel by 25 flags. Each strategy performs an initial scan of some region of the utterance, creating one-word seed events. In \"middle-clut\" strategies the initial scan is done over the entire utterance. In L-R strategies the initial scan only considers words that could begin the cttmnce. In \"hybrid\" strategies the initial scan fixes or) an initial portion of the utterance and then middle-out analysis is done on this region with the rernaillder necessarily being analyzed L-R. In all these op:ions events are ordered on the queue by their priority scores.", |
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| "start": 1068, |
| "end": 1084, |
| "text": "Decet~rber. 1976", |
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| "start": 157, |
| "end": 177, |
| "text": "(/b, d, g, p, t, k/)", |
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| "section": "THE NEED FOR A FRAME,SEMANTICS WITHIN ILINGUlST1CS 5", |
| "sec_num": null |
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| "text": "Appendices: Anrmotated phonological. rules, Format and examples df dictionary files, Result summaries for each token, Performance results for strategy variations, BHGDICT and TRAVEkDlICT listings, Dictionary expansiona user's guide.", |
| "cite_spans": [], |
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| "section": "Speech", |
| "sec_num": null |
| }, |
| { |
| "text": "Munchesrer. England", |
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| "section": "V~~I S T ,", |
| "sec_num": null |
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| "text": "It would be useful if specialized terminological glossaries were organized in B way which reveals the conceptual structure of the domarn glossed rather than being o m i d ACM 20: 495-499,,.1uly 1977 SITAR, a low-cost interactive text handling and text analysis system for nontechnical users, is in many ways comparable to interactive bibliographic search aed retrieval systems, but has several additions features. It is implemented on a PDP/11 time-sharing computer invoked by a CRT with micropragrammed editing functions. It uses a simple command language designating a function, a file, and a search template consisting of the textual string desired and strings delimiting the context in which tho hit is to be deliverad. Computational approaches to language analysis are quite sophisticated while computational unders~nding of language generation is all but non-existent. To get out of this im asse attention must be given to matters of rheto:ic and lexical semantics. The xk of Hal ! iday (givenhew, theme/rheme). Sgall, and Eillmore (schemata, frame) is of partl~.uIar relevance in dealing with these issues. The essay concludes with a discussion of how these problems might be modeled in the Knowledge Representatidn Language (KRL) being developed by bbrow and W inograd.", |
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| "start": 171, |
| "end": 198, |
| "text": "ACM 20: 495-499,,.1uly 1977", |
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| ], |
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| "section": "Builerin of rhe Association /or Lilerary ond Linguislic Cornpuling 5: 26-37, 1977", |
| "sec_num": null |
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| "text": "performed from the vantage of one of tfiese vistas with the effect that the operations behave as if the entire network were composed solely of those nodes and arcs that lie in the spaas of a given vista; all else is ignored. When necessary, spaces can he given all the properties iJormally associated with nodes. In particular, arcs from ordinary nodes may point to such spaces (which are called supernodes). Supernodes are primarily used for encuding higherorder structures, including logical connectives, quantification, and questions. We ahould think of the nprcscntation of the meaning of a word or tent u a lrst of instructions addressed to a canwaist or 'a f iim-maker, thase instructions. imposing amstrainb on how a comic strip or fi.'m strip or movie can be made which will display an image or situation representing what tbe word or text a n \"mean.\" These reprcscntations will havc to deal with:' time schemata (simelbn~~ity, sequence, b pan, calendar), penpcctiv'es (point of view), A time specialist is a program which is knowladgeab!e about time in general and which can be used by a higher leliel program. The time speciahst can deal witb different kinds of temporal specifications, incuding fuzzy ones (e.g. \"a few weeks ago\"): 1) events organized by ~o I (~. Y .", |
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| "eq_spans": [], |
| "section": "Builerin of rhe Association /or Lilerary ond Linguislic Cornpuling 5: 26-37, 1977", |
| "sec_num": null |
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| "text": "2) in terms of ~prcrul r~j e r e n r c . cJr~ui.r (e.g. \"birth,\" \"now\"), 3) h c j o r e / u j i e r chains.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "What Sort of Taxonomy of Causation", |
| "sec_num": null |
| }, |
| { |
| "text": "Three basic types of questions can be answered: 1) Did X happen .at T? 2) When did X happen? 3) What happend at 'i'? Database consistency and error correction arc discussed and the speciaist's treatment of a time-travel story by Robtrt Heinlein is prhented. The problem with frame-like systems is that, on the one hand we have to find sopas mechanisms which give the Bystern some flexibility if prerequisites nor subacts of frames are violared. On the other hand, neither a taxonomy of these prerequisites or substateq t~ proposed by Charniak, nor the addition of \"What-Ifw-rules form satisfying solutions. A possible solution to this problem cunsists in the formulation of general rules describing why certain prerquisites or substotes have to be achieved. These rules should be a~c k c d to frames or frame-statements but they should be used only in cases where the frame alone does When called on (by the response corn nent) to provlde the a n m r a r quation cbs P\" deduction component can, 1) retrieve nformation storad directly in the net$, 2) dcrlva information using general information stored as theorems in the net, 3) all wer supplled functions pointed to in the net that obtain information from knowledpe s o u t a s other t h a~ the net (such as data files). The dcd~iction component rcaptJ ss input a QVlSFA (a &a bein a partition of the network) containing the network translation of an English query and a Z; \\ ISrA containing the knowledge base from which answers to the query us to bs retrieved. Processing entails seeking a match in thc KVISTA for the ueryj pattern. A 4 succcssful match produces s list containing r binding for each QVIS A elemant to r corresponding KVISTA element After a bindings list is rcturncd it a n be repeatedly pulsed to find as many different answers to the query as dcsitd.", |
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| "section": "What Sort of Taxonomy of Causation", |
| "sec_num": null |
| }, |
| { |
| "text": "Dlelag", |
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| "section": "GUS, a Frame-driven", |
| "sec_num": null |
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| "text": "In spontaneous computation (Sc) coae runs spontaneously rather than on demand. A LISPbased SC system in:ludes: cc~mplex trigger pitterns, the organization of trees of trigger patterns, and higher level organialion and control of SC via a \"channel\" to which w u r c h~r s (triggers) or \\c1rvc1r\\ may be sttched at r n p p r~~t r r~ A channel is the medium through which one LlSP function calls another function. SC can be used in cognitive models to model nonalgorithmic inference, to \"follow\" characters in a story ccmp~~chension system, to act as subgonl protectors and plan optimizers in a problem solver. Also discclssed: SC, context and frames: ideas related to partially triggered SCs and their thtclretiml applications as contextfocslsers and motivation-generatom,", |
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| "section": "Spontaneous Computation in Cognitive Models", |
| "sec_num": null |
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| "text": "Comprehension entails iaentifying old concepts in memory and attaching new information to them. Definite noun phrases (DEFNPs) are the most frequently used means of expressing old information. Context plays a crucial role in identifying the reference of DEFNPs and the system uses a focus space partition to represent this context. While the resolution of both pronouns and nonpronominal DEFNPs use global dialog context and immediate context, the; former is more important for DEFNPs, the latter for pronouns. The resolution procedures all depend on the existence 9f a representation of focus of sitention.", |
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| { |
| "text": "The bulk of the chapter concerns the collection and analysis of two types of dial-", |
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| "section": "SEMANTICS-DISCOURSE: COMPREHENSION: SYSm", |
| "sec_num": null |
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| "text": "task-oriented dialogs involvingcommunication between two people cooperating to complete a task; 2:) data-base-oriented dialogs involving communication directed toward obtaining information from a computer base. Diaog analysis reveals that contextual influeaces operate at two levels in p. discourse, 1) The global contextthe tors1 discourse and situational settingprovides a set of constraints which the system uses for the resolution of definite noun phrases by partitioning the network into focus spaces.2) The immediate context of closely preceding utterances is used in the interpretation of elliptical expressions, The semantic component can perform three function% 1 ) ' it may filter out phrase combinations which do not meet semantic criteria 2) For combinations that are acceptable the semantic component may build deep, internal structure representing the meaning of the input (or portions of it) in the context of a particular task domain. As filtering (by both semantics and discourse) is dependent upon the structures assigned to subphrases d the input, filtering and structure building are combined. If any of various checks and restrictions in the structure-building process recognize an anomalous comdition in a structure being built, then the structure building fails, and this failure, acting as a filter, serves to reject the phrase combination.3) The semantic component may make predictions concerning what words or syntactic contructions are likely to occur in other parts of the utterance. This chapter emphasizes 1 and 2, with only a brief treatment of 3. Much of the significance of Montague's work rests upon the acceptability of a second-order functional calculus with a modal aperator, and the extensive model theory based on i t An alternative is suggested with consists of an applied, first-wder logic with the calculus of inclividuals and event logic, and with first-order inscriptional semantics based upon that. The main kinds of' sentences that have been difficult for Montague and his followers arc examined in some detail and accounted for in this alternative logic. the phrase structure declarations, and LISP procedures arc written and compiled to ~mplcmcnt the rule procedu:*es. The Corn iler also builds an internal lexicon that includes special entria for 'multiwords.' Finally, loo ahead information i s coqputed and stored for categories and rula.", |
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| "section": "1)", |
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| }, |
| { |
| "text": "Asa", |
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| "section": "The Proper Treatment of Montague Grammare In Natural Logic and Linguistics", |
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| "text": "A key concept in structured rogramming is abs!ractioi~; the retention of the essential properties of a object and t ! e corollqry neglect of inessential details. Alphord is a programming language which makes use of abstraction mechanisms called J o r~j~s to achieve its goals of supporting the development of well-stiuctured programs and the formal verification of these pro rams. The important roperty of Alphard IS its e.bility to separate the use of an A new way to construct binary search w a s has been discovered by C. J. Stephenson (IBRRB RC 6298, abstracted elsewhere on this fiche) in which the new item that is inserted into the trec becomes the root of the resulting t r m This new method is compared with a previous method which involves two stages: 1) A forest is produced that has keys in order both doa tree and from left to right in the roots of each list of immediate subtrees of a trec 2) A sorted list is produced from the forest by removing the root of the first tree, i.6 the smallest key, leaving forests that are merged into one. The same process is then repeated on the resulting forest. It is possible to construct a binary search tree by inserting items at the root instead of adding them as leaves. When used for sorting the method has several desirable properties, including:a ! fewer comparisons in the best case, b fewer comparisons in the worst case, c) a reduced variance, and d) good performance when the items are already nearly sorted or nearly reverse sorted. For applications in which the tree is searched for existing items as well as having new items added to it (e,g. in the construction of a symbol table), the tree can be made to exhibit stacklike behaviour so that the fewest comparisons are required to locate the most recently used i terns. The D-graph model offers a uniform notation to describe basic data structures like domains and relations, integrity constraints and views. The basic entities in thc model are objects, which are characterized by types. Type specification is used to define the composition of objects out of other objects of different types and monipmtion rules for objects. The eoncept of abstract data types is employed to provide the encapsulation of data objects by 41 the operations applicable to these objects. The concept has been applied to model and implement the synch.ronization of concurrent accesses to shared rcsourccs in operating system8 and for the design of programming languages which support structured and modular programming. It is shown to be suitable for modeling integrity constraints and views and for the manipulation restrictions imposed by constraints and views. In a multi-attribute relation or, equivalently, multi-columq table a certain collection of the projections can be shown to be independent in much the same way as the* factors in a cartesian product or orthogonal comp ntnts of a vector. A precise notion of 'independence for relations is defined and studie 1 . . The main rcsylt states that-the operator which reconstructi the original relation from its independent components is the natural join, and that independent components split the full family of functional dependencies into corresponding corn pone^^ t families. These give an easily checked criterion for independence. Aggregation transforms a relationship between objects into a higher-level object and is important in conceptualizing the real world. An aggregate is a data type which, under certain criteria of \"well-def inedness,\" specifies aggregation abstractions. Relational databases def incd as collections of ageregates are structured as Q hierarchy of n-ary relations. To maintain well-definedness update operations on such databases must preserve two invariants. Wclldefined relations are distinct from relations in third normal form. These notions am complementary and both are. important in database design. A lop-down methodology for database design is described which separates decisions concerning aggregate structure from decisions concerning key identification. Agbrqate types, a@ other types which support nalworld abstractions without introducing implementation debil, s h o~l d be ilrcorporatcd into programming languages. Quaon-Anvering may be regarded as a species of translation: The system Qanslata &e user's query into a formal language, the descriptioti of the query in the formal languaa mstituting a a t of procedures to be carrid out in otder for the query to be answered. and rin action (indtcating conclusions to bc drawn if the premise is satisfied). A tree of corltexts is consrrr~ttcd dyrb:tni icnll y from a fixed hierarchy as the consul tat ion proceeds and rules are invcked in a backward unwinding scheme that roduces a depth-first search of an P AND/OR goal trcc. The system is fast enoi~gh for rea -tirtie interaction and an informal study has been coniptcted in which experts apprqved of 72% of MYCIN's recommendations. The idea that loosely defined simulation models of organizational behavior can yield more significant information than conventional precisely defined ones is explored using NL as the medium. The variables are formulated verbally rattier than mathematically. A generative grammar is presentgd which restricts the set of allowed linguistic values and relations, thus making is povible to formulate a semantic model based on fuzzy set theory. The semantic model can then be used to caiculate the dynamic behavior of verbal models, maki;~g it possible to infer future behavior given a linguistically stated initial state. A model of the general causes and effects of the use of bureaucratic rules (taken from Alvin Gouldner's l'utierns 01 Induslricll Der~~ocrucy) was implenlented in APL.", |
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| "section": "USC lnj'ormafiort Sciolces lnsrirute paper ISl/RR-76-46, 47 pp., June 1976", |
| "sec_num": null |
| }, |
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| "text": "Cogillrive Science 1: 297-j13, July 1977 Forty-nine subjects judged the relevancy of sentence parts of a word problem (the Alls rts problem). Patterns of subjectst judgments suggest three problem-solving heuristics: a GT$ heuristic, a TIME heuristic, and a QUESTlON heuristic. Presgntation of the question before the problem tends to suppress the SETS and TIME heuristics. A computer program (ATTEND) which simulates subjects' behavior is described. It is context-sensitive in that it can change a relevance judgment upon the acquisition of further information. Average subject judgments and ATTEN D 'judgments agree for 87% of the items. ", |
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| "text": "Science 1: 297-j13, July 1977", |
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| "section": "Carnegie-hf ellon U~riversity, Piitsburgh, PA 75213", |
| "sec_num": null |
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| "text": "representations to avoid semantic, distortion. The + experiments investigated both objective and subjective comprehensibility levels of apleratai speech thar was produced by depleting pauses from pre-recorded speech (done under computer control with the Speech Filing System developed at Watson). The comprehensibility of this pause-depleted speech was compared to that of speech carefully read from a transcript at the same rate (Experiment I) and to s eech extempxaneously generated at approximately half the pause-depleted rate (Experiment I I~. The pause-depleted speech was found to be at. least a: comprehensible as the other types, and in certain cases more .comprehensible. This argues for the viability of an automatic process that can reduce the listening time by at leas% 50 percent without reducing thc comprehensibility. 6 / 8 6 , 20 pp., Seprember 3, 1976 Subjects were briefly trained $n the use of dictation equipment and then measurements were made of their performance while dictating 16 business letters. Pause (planning) ti'mes and review times decreased relative to generate times. In the comparison experiments which followed the quality and efficiency of subjects' dictated documents (letters and one-page essays) were equal to those of subjects' written documents, even though subjects had just learned to dictate. Sphking, in which a recipient listens to rather than nads the author$ document, emerged as a potentially useful composition method for Offices of the Future. We desire to have the robot that a n walk about the room, store information about the state of the room and answer questions about the room. And we wish to use natural language to control the robot The robot is built with innate capabilities for physical action and for information processing. The latter problem in broken illto three phases. In the first the words of the languag : are correlated with concepts (initially, with the primitive concepts). Both generative and interpretive semantics assert the necessity for rules of eliminative definition. However, there is no convincing evidence :or the psychological reality of such. Intuitive arguments can be adduced against the reality of eliminative definition and experimental evidence concerning reaction time to achieve a correct evaluation of sentences containing variors types of negatives suggests that such a level is unreal. If our arguments are sound then it appears practicaly mandatory to sssume that meaning postulates mcdiats whatever entailment relations between sentences turn upon their lexical content. The LEARNMORE part of the LAS (Language Acquistion System) program takes as its inputs a sentence, a semantic network representatio~n (HAM) of the sentence's meaning (taken to represent the output of a picture parsing routine), and an indication of the main proposition of the sentence. It then induces an ATN which acts as a map that enables it to go back and forth between sentence and meaning. It induces the word classes of the language, the rules of formation for sentences, and the rules mapping sentences onto meaning, The induced ATN can be u s 4 for both generation and comprehension. Critical to the performance of the program are assumptions that it mates about the relation betweem sentence structure and siirface structure (the graph deformation condition), about when word classes may be formed and when ATN networks may be merged, and about the structure of noun phrases. These assumptions seem to be good heuristics which are largely true for natural* languages although they would not be true for many nonnatural languages. Provided these assumptions are satisfied. LAS seems. capable of learning any context-free language. POLITICS is an automated political belief system simulator. Given a story about a political conflict and an ideolo~gy to use in interpreting the story, POLITICS generates a full story representation using the knowledge structiu~~es of. Schank and Abelson (1977), predicts possible future events, makes suggestions absut what should be done to rectify the( situation, and answers a wide variety of questions. A subset of politics can function like Abelsons Goldwater machine (1965), but it solves most of the serious problems faced by that machine. An ideological belief system is rtprcsented as the attribution of a set of goal trees. Goal directed inferencing processes were developed to inteamuwitla scripts and coun terplanning strategies were investigated,", |
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| "text": "its scmmtica, WinQgradus system is n o t about natural a l l , but absut the q u e s t i o n s of how goals and ~u b -g o a l a are to be otganized fn a problemsolving a y s t e m capable of manipulating simple physical o b j e c t s * . [p.9,9, i t a l i c s in o r i g i n a l ] W k . i l e t h a t may be t r u e a b o u t the robot system, Winograd's own distinction between p a r s i n g and p r o b l m s o l v i n g (.Even though we used the robot s y s t a as our t e a t area, t h e language programs do not depw-d sn m y special subject m t t e r , and a e y have beadapted t~ other uses\". [2O,p.2] ) should not be ignored, especially in a chapter 0.1 parsing. The same mistake is made with respect to Woods8 Augmented Transition N e t w o r l c (ATN) Grammars, While it i s true t h a t , \"both Woods and Winogxnd have argued in p r i n t that their two systems a r e essentially equivalent\" i p . 9 9 , italics in original), and this e q i~i v a l e n c e is accepted by the A1 community, t h e s y s t e m s t h a t are equivalent are the systems not t h e robot system and the lunar rocks qoestion-answering system [ 2 1 ] . Thus i t is quite wrong, relative ko what should be &iscussed ia t h i s chapter, to say t h a t \"both are grammarbased deductive systems, operating within a q u e s t i o nanswerinq environment in a highly limited domair of d i s~o u s s e \"~ [ p . 991 It is also i n c o r r e c t to say about the parsers that \"there is no nee$fa discuss both, and Winograd's is, w i t h i n the A 1 community at least, the better known of t h e t w o \" . [ p . 9 9 ] Indeed, the large m a j o r i t y of A 1 language understanding systems use ATN grammars, and t h e absence of a discussion of them is one of the greatest shortcomings of t h i s book, S e m a n t i~N e t s a s kemory Modelsby Greg Scragg (27-pages) Iq Chis chapter, Scragg introduces and d i s c u s s e s semantic networks from those w i t h arc kabels such as L I K E S , HIT and HAP (has-as-part), which should probably j u s t bc texrned \"reJationa1\" networks, to those w i t h case relations as, arcs, to d i s r , u s s i o n s of quantification, and of procedures ~omputational Semantics in semantic networks, He a t s o t a k e s a few pages each to discuss S c h a n k b and Simmons' networks. Scragg rightly discusses the difference between individuals and classes (he uses the t e , r m s \" t o k e n s \" and \" t y p e s \" ) , and the importance of distinguishing S a t members h i p ftom s u b s e t , a surprisingly often neglected qnd confused p o i n t . However, he c o n t i n u e s a c l o s e l y related confusion by u s i n g the same relation, HAP, between t o k e n s ( a t o k e n of G I R L and a token of HAIR) as between types (the t y p e s BIRD and W I N G ) . This is incorrect because the i n t e rp r e t a t i o n of x HAP y cannot be c o n s i s t e n t . In same cases, it is \"x has y as p a r t \" , and in o t h e r s it must be \"each x h a s a (distinct) y a s Scragg gives some i n s i g h t i n k 0 the d a t a s t r u c t u r e s for implementing s e n a n t i c networks and so encourages the reader to 140k beyond the usual p i c t o r i a l r e p r e s e n t a t i o n . This is important when comparing different network formalisms. For example, as Scragg p o i n t s o u t , Eugene G h a r n i a k ( 2 6 p a g e s ) Charniak b e g i n s t h i s chapter by d i s e u s s i n c j the demon based system of Q4), Me c r i t i c i z e s this approach and a l s o NER (demons are PLmNER a n t e c e d e n t theorems) because of the fixed direction of excitation. For example, if the possibility of r a i n a c t i v a t e s t h e umbrella demon, how do w e understand \"Jack began to worry when he realized t h a t everyone on the street was carrying an umbrella\" [p. l36]? The basic problem seems to be t h a t PLANNER requires one to distinguish one of the propositions of an inference r u l e as a paktern, burying the others inside the theorem. It does not allow all of the propositions to be treated as patterns, as necessary (as is allowed by the semantic network deduction r u l e s of f 181 ) . Next Charniak discusses McDermottb TTOPLE [I4]. The i n t e r e s t i n g features d i s c u s s e d are TOPLE's s e t s of possible worlds and its performing inferences in order to s~p p l y s u p p m t for believPng new inputs. He also discusses RiegerQs inference program [16], concentrating on Rieger's belief in massive read time inferencing and his classification of sixteen types of inferences. Finally, Charniak discusses the influential, though controversial theory of frames. He cdrnpares frames to demons and finds frames preferable. P a p i n q E n q l i s h -l I by Yorick Wilks (30 pages) This chapter is a continuation of Parsinq E n g l i s h I. Apparently, they were originally written as one chapter. then separated for no obvious reason. In this chapter, W i l k s warns that, \" ' p a r s i n g ' is b e i n g used not only in its standard sense in mabhematical, and ccsmputational linguistics\" [p.155], b u t i n c l u d e s building some meaning structure representation of the surface 1anguaqe. T h i s kind of passing Wilks d e f i n i t e l y favors: \"The thesis behind this chapter ... is that p a r s i n g is essential to s system... T h e arqwment i s n o t o n l y that p a r s i n q provides t e s t of proposed s t r u c t u r e , f o r t h a t secondary, b a t t h a t the parsing procedures define what t h e significance of t h e proposed structure is\" lp.179, italics in original]. Milks first d i s c u s s e s three \"second generation\" parsing systems-The keyword and p a t t e r n parser of C o l k i y et a l e ' s PARRY [ 6 : 7 ; 9 ] , Wilks' own preferential semantics system based on \"formulas\", \"templates\" \"parapbates\" hnd \"inference rules\", and ~i e s b e c k k parser for the MARGIE s y s t e m [17] About tep pages are spent on Wilks' s y s t e m , more than any other system discussed in the book. Wilks considers a l l the systems discussed in this c h a p t e r to use \"frames\", Me feels, \"the key p o i n t about any struc&tltures are to be called farme-like is that they attempt to s p e c i f y in advance what is g o i n g to be s a i d , and how t h e world e n c o u n t e r e d is g o i n g to be organizedmm. In psychological ahd v i s u a l t$tms, frame approaches envisage an understander as at Icqst as much a looker as a seer\" [p.155]e H e distinguishes between \"smaII s a b e * and \"Large scale\" frames (large scale frarnes are mat are commonly referred t o as frames or \"scripts*), and has some scepticism about large scale frames, *It is not being argued here that large scale frames have no S m c t i~n , only that, as regards concrete problems of language understanding, their function has not y e t been mde explicitN [p 1831. T h e chapter ends with a nine page comparison of systems ten different dimensions: level o f representation; trality of information; the phenomenBlogical level of bferences; decoupling of parsing and inference: exhibition of ner of appkicLaion to input texts; amout of fqmard inferencing; odularity; scale of representation; connection ui4& real world procedures; justification of adequacy. Psychology of Lanquaqe and Memory by Walter F. Bischof (19 pages) *The i n t e n t of this chapter i s twofold: first it should ptovide the non-psychologist with some b a s i c concepts and soimportant experimental findings in the field of psycholinguistics and the psychology of memory. The second goal is to take a close l o o k at the nature of psychological n t s and psychological evidence' [p, 1851 . This is done from an ahitedly biased point of view: \"the topics ehosen for review were chosen more because of threia: popularity in AI than because of their relative importance in Computational Sema'nli cs psychologym [194]. The topics include association theory, experiments designed to test t h e psychological reality of phrase-structure and transformational rules, memory of meaning versus memory of syntax, short term and long term memory, the Collins and Quillian model [ 8 ] sf hierarchicas hemory organization. Although the discussions are b r i e f , the choices are good f o r the iOtended audience. Bischof is not only salective in h i s view of psychology, but highly skeptical: \"the student o f A f should be able to see from the diseussisn here that the ability of psychology to design and carry out experiments which w i l l give clear and indisputable res%lts is very limited, and t h a t t h e i r ability to provide a safe and clear i n s i g h t i n t o human language understanding is similarly limited\" [p.191). Therefore. \"A1 is well advised n o t to over-estimate the importance of psychological arguments\" [p.201] * by Yorick Wilks ( 2 9 pages) In this chapter, W i l k s is mainly i n t e r e s t e d in discussing and comparing the work of Richard Plontag~e and Ladwig Wittgenstein. \"These philosophers have been chosen not so much f o r t h e i r influence on our s h j e c t matterr whkch has been small, b u t because their views are diametrically opposed on the key issue of formalization\" 1p.2051. His belief is \"that the influence of Wittgenstein ha@ been Largely beneficial while that of Montaque has been largely malign\" [ p . 2051 . F i r s t Wilks introduces some basic topics from the work of Lelbniz, Frege, Russell, early Wittgenstein, Carnap, and Tarski. As in the previous chapter, the discussions are brief but t h e topics well chosen. His introduction to Montague is via the analysis of the sentence \"Every m a n loves some woman\". Montague i s always difficult, but W i l k s ' presentation is relatively easy to follow and is done without introducing the la&da calculus. His discussion of Wittgenstein is intendedr \"simply to give a flavor, to those unfamiliar with h i m , of what Wittgensteic has t o offer\" [p.222! X i s s t y i e is to cover eight topics by presentinq for each a thesis, some quotes from Wittgenstein and some comments. The overall impression is t h a t W i Introduction to Pr by Margaret King and P h i l i p Hayes (47 pages) This chapter is precisely what its title suggestsan introduction to L I S P far either the non-programmer OL the mn-LISPing programer. It takes a modern approach (dotted pairs are not mentioned) and uses examples to which the bookos saudisnce will be able to relate. It a l s o has a good s e t of exercises at t h e end of each section w i t h eolutione at the end, of t h e chapter. O f necessity i t moves f a s t , skimming through many topics including P R O G s , property lists, mapping function8 and FEXPRs, This sheds doubt on how much sf a LISPar t h e naive reader will become a f t e r working t h r a~g h the chapter. L i k e much o f the book, all t h e right topica are covered; but briefly. If one were to design a course on natural language understanding by camputerr and list all the topics t h a t should be coveted, one would find t h a t almost a l l ware inclddad in this book, ATN gramars being one notable exception. However they are al colrered o n l y briefly and from a very d e E i n i t e point o f view. I have j u s t finished g i v i n g such a course u s i n g this book, additional r e a d i n g s , and my o m \"corrective\" viewpoint. The s t u d e n t s felt that t h e book\" ddiscussion of each topic was too brief to be eelb-contained u n l e s s they already knew something a b u t it.", |
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| "text": "The f i r s t f i v e chapters o f t h e book, in which M l l o n goes t l~r o u g h a l o t of l e x i c a l semantics, introduces rn basic notlions of semantics, and d i s c u s s e s t h e relafionship of semantics t o morphology, t o extralinguistic Imowleage, and t o meCaphor, are excel7 ent: he gives a o l e a r , coherent, and acemate presentation o f a well-selected and q u i t e broad-ranging series 3f t o p i c s c Hov~ever, t h e s i x t h c h a p t e r , dealing w i t n f o m a l o g i c , i s so thoroughly bungled t h a t I find it hard t o b e l i e v e +hat it could have been written by t h e same person t h a t produced t h s preceding chapters, 3 should mention st t h e o u t s e t t h a t I have aj;l indirect personal connection with this book, namely t h a t I was t h e first person illon on is t h e t h i r d ) who was .engaged $0 m i t e irb$ B withdrew from the project-.a y e a r a f t e r my deadline, &th 17 pages of manuseript m i t t e n . D i l l o n % book i s in f a c t not ~l l t h a t d i f f e r e n t from whrilt I m i g h t Wvs m i t t e n i$ BaQ heam able t o g e t qg a c t together in about 1973. ( ,~d l l o n , 1 should: point out, did not see aborted -mam~s:ripL, not t h a t it wotoulL have done him any good). Indeed, many of the Sunglee in chapter 6 f o r w M & I! chide Dillon can be a t t e s t e d in my own works af t h e l a t e 1960as, which Dillon drew on in preparing chapter 6. The f a c t t h a t I w i l l devote more space here to saying wha% is wong w i t h chapter 6 t h m w & is goo6 about c h a p t e r s 1-5 and 7 should not be taken as implying t h a t I think the f a u l t s of the one bad chapter outweigh t h e virtues o f t h e o t h e r six. In f a c t , t h e book could be used quite p r o f i t a b l y in s coWac on semantics or i.n sn &.ntioductory linguistic course, provided that t h e i n s t r u c t m either Has his students s k i p chapter 6 e l l t i r e l y o r h a s them read a good deal of supplementary m a t e r i a l on t h e topics covesea these, The pr4ncipsl PaiLings of chapter 6 a r e a failure t o separate t h e many d i s t i n c t questions t h a t arise, systematic errors in .the employment of s t m d n r d l o g i c a l formalisms, bizarre English paraphrases of logical fomulas, and implausible claims about the loeanings o f t h e English sentences used as examples. DLllon appears t o Bccept t h e p o l i c y knovm as 9 . m r e s t r i c t e d quantificstion8, in wKch all vnriablee range over t h o e n t i r e universe o f discourse, and t h e noun of f h e quantified NP is fit i n t o t h g l o g i c a l etructure by using a connective ( 3 in the case of t h e universal qunnti,fier, & in t h e C R B~ o f t h e e x i s t e n t i a l qu8ntifier) t o combine it With t h e @matrixg p r o p o~itiona.1 funct-ion, e . g .", |
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| "text": "a (vx) (~h i l o s o~h e r x 3 Dangerous x) \" A l l philosophers w e dangerous\" b. (3x) (~inguist x & Qbnoxious x) 'Some linguists are cbnoxlsus8 T h i s p o l i c y is t o be contrasted w i t h an alternative not considered by Di'llon, t h e so-called \"restricted quantificationg, i n which each variable ranges over a restricted domain, gibvefl by t h e noun of t h e quantified NP, w i t h a l o g i c a l constituent structure as in ( 2 ) : !'2) a. x Philosopher x ) (~m g e r o u s x) b. (3~: Linguist x)(0bnoxious x) See McCarjley 1972 f o r ar e n t s i n favor of r e s t r i c t e d q u m l i fi c a t i o n as part of a s y s t e m of logic that can o p t i m a l l y be integrated with natlu'al language syntax and linguistic semanti'cs F o r one thing, es$ric$ed quantifier9 w e hopeless as a basis for t h e analysis o f quantifiers other than the logicians8 f a v o r i t e onest P few, 0s almost v i a b l e analysis sf most, a l l would have t o involve some version of restricted quantifice~ P P -When Di'llon s&yys ((p.. 95) \"Generic sentences, tho t h e quantifier is l e a s t h a n univarssl, have a l s o been s y m h l i -d with an entailmeQ-bw', lie conflates the i s s u e o f how genericity i s related t o quantification m d the issue of how quantifiers f i t into logical structure. He ought to have discussed the latter issue long before he took u.p generics, so t h a t he could deal with the matters that are peculiar to generics against a background of clear alternative3 for the treatment of those things t o which generics might be related, His elevatiori of an mcillary issue t o c e n t r a l s t a t u s hers is made particularly glaring by t h e f a c t that his brief discussion of. generics cromas in his pection on lfcomeeLivesw rather th.m the one on qumtifiers, Dillon would have been wiser t o do chapter 6 in terms of res\"cicetad quantifiers rather \"tCm e s t r i c t e d quantifiers, since he would then have been abbe t o avoid mechanical difficulties that are inherent in the use of estricted quanlifiers an$ which he himself has not mastered. Specifically, in representing the meanings of complex sentences, Dillon never gets the m a t e r i a l corresponding t o the nouns in the right place.", |
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| "text": "example, in representing the meaning of (323) he g i v e s the nearly t 4 w t o l o g o u s ( 3b) inStead of the stanaard (an3 plausible) ( 3c) :: Linguictics Semantics ( 3 ) a. Each boy kissed a girl. b. (\\dx)(3 y ) ( (BOY(X) & GTRL(Y) 3 ~Is~(x,y))) C1, (\\~x)(BoY(x) 3 ( ] Y ) ( G~L ( Y ) & KIss(r,y) According t o t h e stand31-d truth conditions, (3b) i s t r u e under v i r t u a l l y a l l circumstances, namely circumstances wder which t h e r e .re e n t i t i e s t h a t a r e no$ girls ( t h e number 10 is not a g i r l , t h e r e f o r e (BOY(X) & G I H L (~O ) ) is f a l s e whatever x i a , whether a boy o r a beanbag, and t l~u s f o r any value of x", |
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| "text": "t o appear in his book c a n be attributed in p a r t t o hiss lack of attention t o t h e giving of r u l e s f o r t h e relationship between l o g i c a l s t r u c t u r e and surface s t r u c t u r e . The most obvious rules f o r mapping e s t r i c t e d quantifier f o m~~l a s onto surface s t n~cturps wo~ald cover t h e case of ( 3 c ) but n o t t h a t of (3b), since ( 3 c ) but n o t ( j b ) congists o f structures like (la-b) embedded in one another. The very first example t h a t he gives o f W analysis involving quantifiers is an analysis of ( 4 a ) as !4b) r a t h e r than as (42): ( 4 ) a. A boy kissed a girl. b. (~X)(~~)((BQE(X) & : C I R L (~) ) & P;ISS(X,Y))) c. (~X)(BOY(X) & GIRL(^) & RISS(~,Y)) While ( 4 b ) a n d ( 4 4 have ex:ictly t h e same t r u t h conditions, it i s ( 4 c ) tha.t f i t s t h e general r~l e s associating estricted quantifier f ornulads w i t h surf ace struct~.i.eo containing quantified NP's. Had his discuesion of (4a) been preceded by t h e material t h a t would need to be covered to make that f a c t obvious, Dillon could scarcely have made such blunders as (3b). One recurring disthrbing f eaturc o f D i l l o n ' s inf omal glosses Lo h i s l o g i c a l f o r m u l & B i s h i s h i g h l y unidiomatic uao b f t h e word one, as when, he g l o s s e s (4b) as 'There e x i s t s one t h a t -i s a boy and t h e r e e x i s t s dne t h a t is a girl such t h a t he kissed 2 h e r v , This same odd locution also o c c w s in e a r l i e r chapters, as in his discuasion o f t h e semantics of Adjective + Noun combinations ( p . 62). where he g l o s s e s as 'one, t h a t %B a car w i t h size g r e a t e~r than average s i z e o f c8rst and as Qme. t h a t is ~m . action t h a t i s undertaken jointly'. In his Adjective + Noun glosses, the one is in f a c t superfluous -( one could omit 'one that i s from t h e l a s t two g l o s s e s and from the other glosses in t h a t section) ; however, t h e onew s I_____ would presumably reappear if Dillon ware t o employ consistently the analysis of relative clauses as derived from coordinate struc'tures that figures in cha-pter 6 and were t o stick t o the style of glosses that he uses f o r (4b). He in f a c t introduces this odd use of one along with his very first example of a -3 .", |
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| "text": "c a l formula, namely an analysis of (5a) as (5b), fo'r which he gives t h e g l o s s ( 5 c ) : ( 5 ) a. dohn k i s s e d Mary. b. ((JOHN(X)) & @ARY($)) & (HISSED(X,~))) c. One i-s called John and one is c a l l e d Mary and he kissed her* If t h e __I one's a r e interpreted liternllg, ( 5 c ) expresses n r a t h e r bizarre p r o p o s i t i o n t h a t h a s no relztionshia t o (58,). A signifi t a n t -improvement could be o b t a i n e d by replacing 110th occurences o fone by someone, but t h e n the g l o s s w o u l d correspond t o 8 formula involving t w o existenticl qumtif'iers, and D i l l o n makes c l e a r i n t e r t l l~i t h e does not, want t o inc1ud.e any quantifiers in t h e ~~a l y s i s o f ( 5~) . In f a c t , Dillonus use of one is only an ohscure way o f repeating the very thing t h a t it is supposed t o ' e x p l a i n t , namely t h e indexx o r x, and his glosses would have been much c l e a r e r i f he had s i m p l y m i t t e n f% s a n d -9 ' s in them. Dillon' s discussion o f ( 5b) i s u n c l e a b and/or inconsistent as t o whether t h e x a~d y are constsntC o r v z r i a b l e s . The absence o f a quantifier and t h e c o n t r a s t th:.;t h e draws ( p . 87) between ( 5 b ) and s t m c t u r e s involving q u m t i f i e r s suggest t h a t t h e y are constnxts. IIowever, h i s t a b u l a t i o n o f \"seven distingui s h a b l e situntionsw t h a t t h e negation o f (5b) is compatible w i t h c o n s i s t s of glosses that would make l i t t l e sense if x rind g were conslan.ts, e,g. \"One k i s s e d one c a l l e d Mary, but he wasng -b c2,lled Johnut: f r o m t h e f a c t t h a t L a r r y k i s s e d Mary and Larry isn't c a l l e d John, it does not f o l L o w that J o h n didn't kiss Ili~ary (perhaps t h e y both kissed M ) The different situa.tions t h a t he i s d i s t i n g u i s h i n g really involve a t h i r d index, one corres-p0ndi.n~ Lo t h e event of kissing, Some o f t h e seven situations in stic Semantics 29 the list involve B presupposition f h a t an a c t o f kissing took place and give specifications of who the participants in,that mre; others do not involve such a-presupposition. !!51 an& 7, whioh I rate in general as verg well done. In these chapters Dillon discussee componenaal analysis, productive and non-productive word-formation, metaphor in terms of a large number of well-chosen examplee, I o n l y one r e a l l y major gap in the s e t of topics covere&, ely presupposition. While the term 'presxpposition8 oUccurs in sweml mlaces in the t e x t , D i l l o n contents h dBfjLsing the reader that the t e r n has been used in a nmber of afferent senses and is involved in .ongoing controversies, Bpld mfers the reader to supplementary readings fon further enlightent. The various notions t o which that term has been applied m e 04 sufficient importance in linguistic semantics s n d a r e of relevance t o so m a n y of the examples and theoretFca1 points that he takes up t h a t Dillon can hardly expect instructors using his book t o avoid serious disdussion of it, Dillon~s4discussion of metaphor is distinguished by its use of a large body of interesting examples and its avoidance of the hackneyed examples (such as He danced his did, which is not a metaphor at all) that usually fi discussion of metaphor. However, in t h i s eenerally enlightened prasen-1;ation there are two e r r o r s for which I wish t o t a k e 'Dillon t o task. F i r s t , he speaks o f metaphor as r e s t i n g on 'some ~o r t of i n~o r n p a t i b~l i t y between the usual senses of the word and t h e context* ( p . 39). But if metaphoric uses were restricted t o . -1 bvJkboAtD ni-th which a l i t e r a l use was incompatible, rn expression coula never be ambiguous as t o wliether i k is t o be interp r e t e d literally or metaphorically, dhereas in fact such ambim i t g is quite common (Reddy 1969). Indeed, as T e d Cohen L (lecture at University .of Chicago) p o i n t s out, t h e r e a r e metap h o r i c sentences t h a t express truisms when interpreted literally No man i s m islarrd). Secondly, the adjustment involved in ( e b . i . inte~preting a metaphor i s not ( a s D i l l o i a q s it is) the canc e l l ,~t i o n of semantic features but the assignment of a nonstandard referent; f o r excmple, I &-isagree with Dillon's statement t h 2 t t h e i n t e r p r e t a t i o n . o f morsel in t!3road-fronted Caesar / When thou was% h e r e abput t h e ground, I was / A morsel f o r a monarchv ( 1.v. 29-31) involves %uppressing t h e SMALL B I T OF FOOD component i n f a v o r o f t h e associated DELICACY conponentn ( p . 40): Cleopatra hereis speaking of hers e l f as a snack f o r t h e emperor, and t h e reader transfers the r e f e r e n c e (though not t h e sense) o f t h e VXTINGw or MCONSU161NCu' component t o a d i f f e r e n t medim r a t h e r t h m simply suppressing i t (see aga.in Reddy 1969). Useful nroblems are given at t h e e$d of each chapter, with suggested answers given at t h e end o f t h e book, There is a l s o a glossary t h a t i a q u i t e us~ful thouzh flawed in some respcabs, chiefly incompleteness, as w h~ Dillon declines to even h i n t at a d e f i n i t i o n of 'presupposition* and simply refers t h e reader to o t h e r literature, and when he d e f inea a achievement verb' ( t h e term i n from Venalen. 1957) by telling t h e rectder everything that it i s n a f and leavirtg t h e reader t o d c ? t e~i n e by elimination what ' it is, The definiticm o f c o n n e c t i '~ contains a statement %ha% confuses an important issue: tfConneqtives are'held t o be predicates by some, but 00% by logici'ans, because predicntee coabine w i t h ar e n t s t o form propositions, bb-b connective8 combine propositions to form larger propositionsM ( p . 124). D i l l o ' n has-given no reason f o r supposing t h a t propositions o m n o t e b e arguments of predicates and thus f o r not taking connecti v e s t o be simply a s p e c i a l kind o f predicates. Indeed, 'not t o a l l o w propositions to serve as ar e n t s ,is t o cornit mesels, t o t h e schizophrenic position (Prior 1971) % h a t verbs such as know -7 and believe are \"predicates on t h e l e f t m d eoranec%ives on %i-ea r i g h t \" . Dill-oh coneiatently refers t o his conditional connective an \"'entailment\" but usually uses it in a way t h a t would o n l y make sense if it were a material ~~n d i t i~n~l rather than en$ailman%, He doea not advise t h e readgr of t h e distinction between t h e truth-functional material conditional connective th:-tt *f iwree in most l o g i c t e x t s and-t h e relation (not r e a l l y a q t c o m e c t i v e w ) of Linguistic Semantics entailment ( A entails B if B is t r u e in a l l s t a t e s of affairs ' n which A i s true", |
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| "text": "PHILOSOPHICAL FOUNDATIONS The Past, Present, and Future of Computational Linguistics David 6 . Ways Departnlenr of Linguistics, SUNY a! Rlr f/alo, Atnherst, N Y 14260 Papers in Cotnputationul Linguisrics, Akademial Kiudo, Budapesi: 583-585, 19771", |
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| "text": "e . . . . . e e b r n b . e e o . e . o b b~. . . e b . . of these papers ap ed on MCL Microfiche 1.", |
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| "text": "On Somc Relationships of Linguistics' rand lnfonnation RctrievaP, P.SgalI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Understanding Syetems: Final Report, Navemb 1974 October 1976 Volume 11: Acoustic Front Eml William A. Wooda, et ol. Solr S~r a n~k and Nramcm Inc., 50 Moulran SI., Cambridge, MA 02138 Report 3438, 91 pp., D~repber 1976The Initial signal processin component computea the following type^ of puam crossings, 2) LP analysis ( the 0-5 kHz rcgion), 3) speftral energy, 4) formant ex ' 1 fundamental freque y. Acoustic-Phonetic Recqnitlon (APR) SEGMENTATION, which employs a segment lattice to handle alternative LAIIELINO, which arrives at a rough phonetic cheracterization of each SCORING, to determine a score for the correspondence of each phoneme possibility for clch segment The speech synthesisby -r ulc progrsm is used for resppnse grnuation and, mom importantly, for word verification. The phonologial component make use of syntactic md lexical information (and, potentially, semantic information) and outputs to componenL The verificat~on component contairs an lanalysis-by-synthsis overcome inaccuracies present in preliminary phonetic analysis and to t3ke account of the cffccu of the phonological rults. Appendices: Dictionary Phoneme$ APR labels APR m14 Parametera for Scoring. PHONETICS-PHONOLOGY: PHONOLOGY Allophonic Variations of Stop Consonants in a Speech Synthesis-by-Rule Prograh W. A. Ainsworth, and J. R. Millar L)cp{~r/rnent of Ccun~nrrnicatio~~, Unirlursiry u i K t Keele, Sra ffs., U.K.", |
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| "text": "not allow sufficient understnnding. Toward this end five new links between frames are Introduced: normally produced for, partici \\ anant s in, reason, caused and caused by, while. Tea d inference rules of three types are propose . The first type of rule helps to decide why the actio:~s mentioned in the text have been accomplished. The second type of rule is used to figure out how those actions or subseates might have b a n achieved. The third type allow prediction of consequence$ of actions and states. SEMANTICS-DISCOURSE: MEMORY Natural Language Understanding Based on a Freely Associated Learned Memory Net Sara R. Jordan Cotltpurer Science Department, University of Tennessee, Knoxville: lnr~rrtational Journal of Computer and Information Sciences 6: 9-25, March 1977 METQA (MEchanical Translation and Questio~l Answering) accepts unsegmentxl input strings of NL frotr a human trainer and, after processing each string, outputs a NL response. The built-in structure of METQA consists of: 1) the capacity to build a network of nodes and labeled arcs, and 2) the general procedure of categorizing memory nodes into c l a s s accprding to their behavior and usage. Link types: transform, combination, description, class, membership/subset, equivalence. Each node is a List representing a state d some word or phrase during its processing. The structure of the memory itself is independent of the subject matter. METQA learns by comparing response output with any (specially marked) feedback string the trainer may give. The program then determines which, if my, portion of the original input string, was processed incorrectly and appropriate memory modifications arc made. Intelligence can k modeled as a society of communicating knowltdge-based problem-solving experts. Each of these experts can in turn be viewed as a sxiety (hat can be further decomposed in the same way until the primitive actors of the system are reached. We arc corcerned with the ways in which actor message passing can be used' to understand control structures as patterns of passing messages in serial processing. Actors are defined by their behavior and they interact on a purely local level. To set up such a system onc muse 1) decide what kilds of actors to have, 2) decide what kinds of messages each actor can process, and 3) decide whal each actor is to do with its messages. Also discussed: PLASMA (PLAnner-like System Mxfeled on Actors), Event diagrams graphic notation for displaying relationships among events of actor computation.Ston ford Research Insrirure, Rlrnlo Pork, CA 94025 Speech llnd~rstonding Ri#search. Nnol Technical Reporr, 15 Ocrobcr 1975-11 Ocrobcr 1976, X II -1 ro X i / -6 0 , O c t o b~r 1976", |
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| "content": "<table><tr><td/><td>N</td><td/></tr><tr><td colspan=\"3\">Indiana University --Purdue University</td></tr><tr><td/><td>F o r t Wayne</td><td/></tr><tr><td/><td>PRENTICE-HALL, JNC.</td><td/></tr><tr><td colspan=\"3\">Englewood C l i f f s , New Jersey 07632</td></tr><tr><td>xx + 150 pages $6 95 paper</td><td>1 9 7 7</td><td>LC 76-41853 ISBPI 0-13-479464-9</td></tr><tr><td colspan=\"3\">REVIEWEO BY J A M E S D. com~.nding l e a d in t h e National League W t i l ear2.y</td></tr></table>" |
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| "text": ". . . . . . . . 34", |
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| "content": "<table><tr><td/><td/><td>c r o f i c h e 72 : 33</td></tr><tr><td colspan=\"2\">C U R R E N T B I -B L I O G R A P H Y</td></tr><tr><td>PHILOSOPHICAL</td><td>F O U N D A T~O N 34%</td></tr><tr><td colspan=\"2\">SPEECH UNDERSTANDING.</td></tr><tr><td/><td>),</td><td>(~ordrecht :</td></tr><tr><td colspan=\"2\">~e i a e l ) , 498-544.</td></tr><tr><td colspan=\"2\">143-160. A190 i n Z. Vendler,</td></tr><tr><td colspan=\"3\">I\"E8,ca: Cornall University Press, 1 9 6 7 )~ 97-121.</td></tr></table>" |
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| "text": ", , . . . . . 44 PHONETICS-PHONOLOGY . . . . 46 . . 47 LEXICOGRAPHY-LEXICOLOGY . . 48 DICTIONARY. . , . . . . . 49 TEXT HANDLING , . . . . . 4'4 SRAMMAR PARSER. . , , . . . . . . 51 COMPUTATION . . . . . . . INFERENCE , . . . . , . . . . . . . . . B~A I N THEORY. . . . . . . . . . 53 CLASSES & CONSTRUCTIONS . 54 SEMANTICS-DISOOURSE . . . 55 The production and editorial THEORY . . . , . . . . . 56 s t a f f thanks Martin and,Iris COMPREHENSION . . . . , 6 1 . . . , . . . . . 65", |
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| "content": "<table><tr><td>INFORMATION STRUCTURES,</td></tr><tr><td>DOCUMENTATION</td></tr><tr><td>ABSTRACTING c INDEXING,</td></tr></table>" |
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| "num": null, |
| "html": null, |
| "text": "of Techno!ogjl, Catnbrldge, M A 02139 Cogttifive Science I : 84-123, January 1977", |
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| "text": "I propose a four-way schema with psychorogy, computation, formal linguistics, and descriptive linguistics at the poles. ~sycholbgy and computation are about performance; formal sciences are abstract, and psychology and descriptive linguistics are sciences. Psycholinguistics joins psychology with linguistics. Correspondngly, on the abstract side computational linguistics joins computation with formal linguistics and also seems a fruitful area. The most likely place to arrive at s working idea of how competence and performancealgorithms and informationare related is computational linguistics. The more we achieve on the formal, abstract side, the better the chance of formulating goals and criteria for linguistics that will help the linguist decide whether a grammatical invention merits prolonged study.Five sections: Lang~~age. Computation. Computational Linguistics, The Architecture of Correspondence, Control systems. The traditional branches of mathematics analyze struct in which time may play a role, but the analysis does not employ time, Algorithm theory time as an analytic variable. Computational linguistics is the algorithmic analysts of language. Control systems operate in time; they receive information about a proms as it gccurs and return information that inf,lyences its continuation. The study of control systcma, cybernetics, must therefore, like computational linguistics, use time as an an lytic variable Language is a vehicle for both social and personal control. Computational 1 1 nguistics must", |
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| "content": "<table><tr><td>Science and Culture 28: 1426-1432, December 1976 ( S c i e~~c i a o Culruru)</td></tr></table>" |
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| "content": "<table><tr><td>.</td><td>*</td><td>.</td><td>.</td><td>.</td><td>*</td><td>s</td><td>.</td><td>b</td><td>e</td><td>.</td></tr></table>" |
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| "num": null, |
| "html": null, |
| "text": ". . . . . . . . Systems . . . . . . , . . . . . . . . Lcxicogrsphic Progrc~s . . . . . . . . Computer-aided Editing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . , . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... . . . . . . , , ., . . . .", |
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| "num": null, |
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| "text": ". . . . . . . . . . . . . . . . . . 30 Kunzc ., . . . . . . . . . . . . 3 Sgoll . . . . . . . . . . d l Slrreidey . . . . . . . . . . . . . . . . 3 2. Kunze . . . . . . . . . . . . Bicn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41.", |
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| "content": "<table><tr><td>GENERAL t JOURNP.1</td></tr></table>" |
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| "text": "Howard R. Smith Departmet~t of Cotnputer Sciences, The University of Texas ar Austin, 78712 Deparrn~et~l of Cornplrter Science, SUNY 01 Buffalo, Technical Report 119, h;larch 21, 1977", |
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| "num": null, |
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| "text": "Bolt Ijeranek nnd Ne~vntan lnc., 50 ~lloralton Street, Col-t~bridge, 'o. 3438, 110 pp.,", |
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| "num": null, |
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| "text": "LEXICOGRAPHY-'LEXICOLOGY t TEXT HANDLING of linguistic performance and an algorithm for the construction of a MAFP for any normal-form grammar is resented. Since hiAFPs are minimally argrncnted their I .push-down store) is limited to just those situatiolis la which that fowu is necessary f ; ; ;L'ective parsing. They do not nKd to use their PRS to parse a11 noun phrases within :cntences. The readjustment of the surface structure of sentences with LE or RE greater than 3 or 4 suggests that the RE and LE structures are unacceptable and that unacceptability is due to the limited amount of PDS available to the MAFP in the human sentence recognition device. It is argued that Readjustment Rules (whose formal structure is quite different from that of transformations) belong to a distinct component of the grammar which relates syntax and phonology and a readjustment rule schema is The first grammar (SPEECHGRAMMAR) processed words by their usual parts of swech and constructed ordinary syntactic parse trees for a wide variety of complex constructions. The newer grammars use \"semantic\" categories on their arcs as well as 'the traditional syntactic ones; and BIGGRAM and MIDGRAM also incorporate pragmatic information.. Prosodic information is handled by marking those arcs expected to be accompanied by prosodic boundaries. Appendices: L'isting of MIDGRAM Grammar,", |
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| "content": "<table><tr><td>GRAMMAR: GENERATOR</td></tr><tr><td>Chapter XIII:</td></tr><tr><td>Ph. D. Program i n Linguistics, C.U.N.Y. Graduate C e~e r , 33 West 42nd St., New York,</td></tr><tr><td>I0036</td></tr><tr><td>Linguistic Inquiry 6: 533-554, Foll 1975</td></tr><tr><td>Parser Trace,</td></tr></table>" |
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| "num": null, |
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| "type_str": "table", |
| "content": "<table><tr><td>Report No. lSI/RR-77-52, 61., J~n u a r y 1977</td></tr></table>" |
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| "num": null, |
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| "content": "<table><tr><td>SEMANTICS-DISCOURSE: THEORY SI\",MANTICS-DISCOURSE: COMPREHENSION</td></tr></table>" |
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| "text": "is a dialopue system designed to act as a travel agenL it car! tolerate mixed initi~tivs dialogue and its expectations about rpture input evolve in the course of conversation. Tbe system consists cf a morphological analyzer, a syntactic analyzer, frame reasoner, md language generator, all tied to ether by an overall asynchronous contrul mechanism. b nouon of frame implemented f n the system bear3 a family resemblance to Minsky's notion, but the relationship is only that, familial. A jrutnr consists of a nr7t.r~ (which is primarily a mnemonic device for the system builden , a reference to a )rorat!.pc frame, and r wt of To conduct a dialogue the system creates an instiinx of a dialogue frame and bgiar to fill slots for the instance En a m d a n c e with the specifications in the prototype. several years. Like SAM, FRUMP is a script based undcrstander, But FRUMP is a newsraper skimming program, using 'sketchy' scripts rather than full scri ts, rather than a program that carefully reads text. In reading stories FRUMP decides whe t . ! er the stories are new or updates of news even'ts that it has already seen, and stores the important information from the article. FRUMP can then give information on ca news event by mpans of different length summaries. FRUMP can understand and produce a brief summary of a 150", |
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| "content": "<table><tr><td>SEMANTICS-DISCOURSE CClMPREHENSIQN:</td></tr></table>" |
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