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"" }, { "first": "Philip", "middle": [], "last": "Resnik", "suffix": "", "affiliation": {}, "email": "" } ], "year": "", "venue": null, "identifiers": {}, "abstract": ", for the first time the program included a number of presentations by students describing interesting dissertation work in progress. Their papers are gathered in a separate section at the end of this Proceedings. The program committee found its job to be quite difficult this year because of the extraordinarily high quality of the submissions we received. Because of the high overall quality, we have expanded the number of papers we accepted this year, and even so we felt that some papers were rejected that would have met the standards of past years. It gives all of us great pleasure to see the field evolve and its standards of excellence increase. The entire computational linguistics research community deserves congratulations for this encouraging development. We feel this program demonstrates excellent progress in all areas of computational linguistics, and it reflects the increasingly international character of the research community, with contributions from Europe, Asia, the Middle East, and North America. We hope that you find these papers as interesting and exciting as we did. As Program Chair, I wish thank our invited speakers, Sue Atkins, Charles Fillmore, and Jun-ichi Tsujii for their contributions to the program. I also thank Cecile Paris for organizing the tutorial sessions, and", "pdf_parse": { "paper_id": "P91-1000", "_pdf_hash": "", "abstract": [ { "text": ", for the first time the program included a number of presentations by students describing interesting dissertation work in progress. Their papers are gathered in a separate section at the end of this Proceedings. The program committee found its job to be quite difficult this year because of the extraordinarily high quality of the submissions we received. Because of the high overall quality, we have expanded the number of papers we accepted this year, and even so we felt that some papers were rejected that would have met the standards of past years. It gives all of us great pleasure to see the field evolve and its standards of excellence increase. The entire computational linguistics research community deserves congratulations for this encouraging development. We feel this program demonstrates excellent progress in all areas of computational linguistics, and it reflects the increasingly international character of the research community, with contributions from Europe, Asia, the Middle East, and North America. We hope that you find these papers as interesting and exciting as we did. As Program Chair, I wish thank our invited speakers, Sue Atkins, Charles Fillmore, and Jun-ichi Tsujii for their contributions to the program. I also thank Cecile Paris for organizing the tutorial sessions, and", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Abstract", "sec_num": null } ], "body_text": [ { "text": "The ability to generate natural language utterances is an important component of many intelligent systems (expert systems, intelligent tutoring systems, advice-giving systems). This tutorial will provide an in-depth survey of the branch of computational linguistics known as natural language generation. Most of the work in natural language processing has concentrated on understanding text. Instead, we look at the problems involved in generating text. Generation brings up issues not apparent in understanding. The task of an understander is to recognize which choice has been taken. In contrast, a generator must decide why to make one choice over another. Considering generation forces the researcher to come to terms with issues concerning what kind of information must be available to the generation component, where that information may be obtained, and how information should be presented to different users in different situations. In this tutorial we concentrate on a portion of the generation process known as text planning, which is responsible for deciding what is to be said and how it is to be structured. We look at how the content of a text can be chosen (including topics in user modeling and text planning formalisms) and how texts should be structured in a coherent fashion (including topics on text structure and coherence, pragmatics, focus of attention).", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "", "sec_num": null }, { "text": "Current interest in spoken language systems has focused attention on potential interfaces between traditional concerns of Natural Language Processing with syntactic, semantic and diseourse/pragrnatics representation and analysis -and traditional concerns of speech scientists and engineers with speech recognition and synthesis. One area of mutual interest is intonational variation. How do intonational features such as phrasing and prominence interact with syntactic, semantic and discourse factors to shape the overall 'meaning' of an utterance? (Can parsers be designed to parse intonational features along with lexieal items? Can intonation disambiguate among possible semantic interpretations of a sentence?) How can knowledge of intonational regularities improve speech recognition techniques and provide more natural-sounding synthetic speech? (Can intonational information be incorporated into recognition hypotheses? Can likely intonational features be reliably predicted from text to approximate human intonation in synthetic speech?) This tutorial will survey (a) current empirical and theoretical research on the contribution of intonation to utterance interpretation, Co) methods of prosodic analysis from speech corpora, (c) alternative approaches to intonational description and representation, and (d) current and potential applications to speech generation systems, text-to-speech systems and speech recognition. The tutorial will be extensively illustrated with examples from natural and synthetic speech.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Intonation in Spoken Language Systems Julia Hirschberg, AT&T Bell Laboratories", "sec_num": null }, { "text": "Recently, panels and sessions at COLING, and conferences of the Association for Computational Linguistics, the Association for Computers and the Humanities, and the Association for Literary and Linguistic Computing have addressed the increasing merging of methodologies in the fields of computational linguistics and humanities computing. On the one hand, computational linguists are devoting considerable attention to statistical and other quantitative measures traditionally used in humanities computing. Also, work with large text corpora, long the central activity in humanities computing, is becoming an important area for computational linguistics. Computational linguists are now beginning to consider texts, and even literary texts, as an object of study and a rich source of information about the phenomena of language and discourse. On the other hand, humanists are turning to methods for morphological, syntactic, and semantic analysis developed by computational linguists to enhance their strategies for literary and linguistic studies. This tutorial will describe work which falls at the intersection of the fields of computational linguistics and humanities computing, either in methodology or use of materials, and show how these methods and materials benefit both disciplines. In particular, work in the areas of computational lexicology and lexicography, corpora and corpus linguistics, statistical models and methods for language and text analysis, and content analysis will be considered.", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Computational Linguistics Methodologies for Humanities Computing Nancy M. Ide, Vassar College", "sec_num": null }, { "text": "Machine Translation (MT) is the area of computational linguistics with the longest history and with the largest volume of dedicated R&D resources on the global scene. After reviewing the primary objectives and accomplishments of MT in its 40-year history, the major MT paradigms will be presented in some detail, including syntactic transfer, semantic transfer, and interlingua-based approaches. Then, we will discuss the appropriateness of these methods to different application areas, including technical vs nontechnical text, specialized domains vs general text, multilingual vs bilingual requirements, spontaneous discourse vs prepared text, and full-translation vs text scanning vs fact extraction. We will also touch upon evaluation of MT systems and recent developments in MT such the re-emergence of statistical approaches, making knowledge-based interlingual MT systems practical, and the integration of MT with other technologies such as document production, optical character recognition, and speech understanding. ", "cite_spans": [], "ref_spans": [], "eq_spans": [], "section": "Machine Translation: An In-Depth Tutorial Jaime Carbonell, Carnegie Mellon University, and Yorick Wilks, New Mexico State University", "sec_num": null } ], "back_matter": [], "bib_entries": {}, "ref_entries": { "FIGREF0": { "type_str": "figure", "text": "Discovering the Lexical Features of a Language Eric Brill, University of Pennsylvania", "uris": null, "num": null }, "FIGREF1": { "type_str": "figure", "text": "Resolution of Collective-Distributive Ambiguity Using Model-Based ReasoningChinatsu Aone ...................................................................................................................................................... 1 Inclusion, Disjointness and Choice: The Logic of Linguistic Classification", "uris": null, "num": null }, "TABREF0": { "html": null, "type_str": "table", "num": null, "text": "Lexical Disambiguation: Information Sources and their Statistical Realization Ido Dagan, Technion Non-Literal Word Sense Identification through Semantic Network Path Schemata Eric Iverson & Stephen Helmreich, New Mexico State University McCoy, University of Delaware, and Joharma Moore, University of Pittsburgh", "content": "" } } } }