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This collection of papers forms the point of departure of an interdisciplinary workshop on Theoretical Issues in Natural Language Processing sponsored by the Mathematical Social Sciences Board. The impetus for such a workshop, bringing together researchers and students from computational linguistics, psychology, linguistics and artificial intelligence, was a desire to provide a forum at which people with different interests in, and consequently different emphases on, the problems of natural language understanding, could learn of the models developed and difficult issues faced by people working on other aspects of understanding. It was felt that an exposure to different aspects and emphases would have a very beneficial effect on all fields of natural language research, and that without such an interchange the potential for much of that research would not be realized. The idea behind this early circulated volume of position papers was to familiarize all the participants, speakers and audience alike, with the current ideas and paradigms in natural language understanding -- their evolution, scope and deficiencies. Specifically, the contributing speakers were asked to address such questions as:
- What computational models and mechanisms have been proposed up to now in these areas?
- What aspects of human language behavior are they meant to account for?
- Are these models compatible?
- Is there a single global view of language understanding and use that is adequately modelled by some combination of them?
- Are there still significant aspects of human language use which they cannot account for?
- What is the best model of human language use that can be assembled out of the concepts that have been developed in computational linguistics, linguistics, psychology and artificial intelligence?
- How well does it really approximate what humans do with language?
- With respect to gaps in that model, is there anything currently in the wind adequate to complete them?
Where speakers were not able to get us their position papers in time, their papers will appear in a supplement to this volume to be available at the workshop and to be included in all copies of this volume subsequently distributed.
- Augmented phrase structure grammars consist of phrase structure rules with embedded conditions and structure-building actions written in a specially developed language. An attribute-value, record-oriented information structure is an integral part of ...
- About fifteen years of active research in natural language question-answering systems has provided reasonably concise and elegant formulations of computational semantics for understanding English sentences and questions about various microworlds. These ...
- Theoretical linguists have in recent years concentrated their attention on the productive aspect of language, wherein utterances are formed combinatorically from units the size of words or smaller. This paper will focus on the contrary aspect of language, ...
- This paper is a spin-off of our work on actors. We have worked out a dictionary for translating between what Minsky et. al. are saying about frames and what we are saying about actors. Using PLASMA [PLANNER-like System Modeled on Actors] ...
- The notion of a commonsense algorithm is presented as a basic data structure for modeling human cognition. This data structure unifies many current ideas about human memory and information processing. The structure is defined by specifying a set of proposed ...
- Using knowledge to understand. Minsky's frames paper has created quite a stir within AI but it is not entirely clear that any given researcher who would agree that the frames approach is correct would agree with any other researcher's conception of what exactly that meant. What is a frame anyway? It has been apparent to researchers within the domain of natural language understanding for some time that the eventual limit to our solution of that problem would be our ability to characterize world knowledge. In order to build a real understanding system it will be necessary to organize the knowledge that facilitates understanding. (p. 117)   [Full text http://acl.ldc.upenn.edu/T/T75/T75-2023.pdf]
|SESSION: Memory: part I. Natural language input||Ann Robinson|
|Augmented phrase structure grammars||George E. Heidorn||1-5|
|Diagnosis as a notion of grammar||Mitchell Marcus||6-10|
|Computational understanding||Christopher K. Riesbeck||11-16|
|The clowns microworld||Robert F. Simmons||17-19|
|On understanding poetry||D. L. Waltz||20-23|
|The reasoner and the inferencer don't talk much to each other||Robert P. Abelson||3-7|
|Automatic planning from a frames point of view||Richard E. Fikes||8-11|
|Syntactic processing and functional sentence perspective||Martin Kay||12-15|
|What makes SAM run?: script based techniques for question answering||Wendy Lehnert||16-21|
|SESSION: Representing knowledge: part I. Primitives||Jon Allen|
|A system of semantic primitives||Ray Jackendoff||24-29|
|Comments on lexical analysis||George A. Miller||30-33|
|The primitive acts of conceptual dependency||Roger C. Schank||34-37|
|Primitives and words||Yorick Wilks||38-41|
|SESSION: Memory: part II. Organization||Jon Allen|
|Organization and inference in a frame-like system of common sense knowledge||Eugene Charniak||42-51|
|The trouble with memory distinctions||Allan Collins||52-54|
|How episodic is semantic memory?||Andrew Ortony||55-59|
|SESSION: Natural language generation and belief systems||Yorick Wilks|
|The phrasal lexicon||Joseph D. Becker||60-63|
|Generation as a social action||Bertram C. Bruce||64-67|
|Speaking with many tongues: some problems in modeling speakers of actual discourse||John H. Clippinger||68-73|
|The boundaries of language generation||Neil M. Goldman||74-78|
|A formalism for relating lexical and pragmatic information: its relevance to recognition and generation||Aravind K. Joshi & Stanley J. Rosenchein||79-83|
|Meta-compiling text grammars as a model for human behavior||Sheldon Klein||84-88|
|SESSION: Representing knowledge: part III. Frames||Eugene Charniak|
|Some thoughts on schemata||Wallace L. Chafe||89-91|
|Bad-mouthing frames||Jerry Feldman||92-93|
|Stereotypes as an actor approach towards solving the problem of procedural attachment in frame theories||Carl Hewitt||94-103|
|Minsky's frame system theory||????||104-116|
|Using knowledge to understand||Roger C. Schank||117-121|
|SESSION: What is a valid methodology for judging the quality of research in computational linguistics?||Daniel Dennett|
|Considerations for computational theories of speaking: seven things speakers do||John H. Clippinger||122-125|
|Improving methodology in natural language processing||William C. Mann||126-129|
|Methodology in AI and natural language understanding||Yorick Wilks||130-133|
|Some methodological issues in natural language understanding research||W. A. Woods||134-139|
|SESSION: Representing knowledge: part IV. Non-linguistic forms.||Andee Rubin|
|Does a story understander need a point of view?||Robert P. Abelson||140-143|
|Creativity in verbalization as evidence for analogic knowledge||Wallace L. Chafe||144-145|
|On retrieving information from visual images||Stephen Michael Kosslyn||146-150|
|The nature of perceptual representation: an examination of the analog/propositional controversy||Stephen E. Palmer||151-159|
|Do we need images and analogues?||Zenon W. Pylyshyn||160-163|
|Afterthoughts on analogical representations||Aaron Sloman||164-168|
|SESSION: Memory: part III. Reasoning and inference||Allan Collins|
|Bridging||Herbert H. Clark||169-174|
|Formal reasoning and language understanding systems||Raymond Reiter||175-179|
|The commonsense algorithm as a basis for computer models of human memory, inference, belief and contextual language comprehension||Chuck Rieger||180-195|
|Understanding human action||Charles F. Schmidt||196-200|
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