From nobody@cs.Buffalo.EDU Tue May 27 14:41 EDT 1997 From: nobody@cs.Buffalo.EDU Date: Tue, 27 May 1997 14:41:13 -0400 (EDT) To: techreps@cs.Buffalo.EDU Subject: techrep: POST request Content-Type: text Content-Length: 3339 Comments: I won't deposit the file till I know the TR number. ContactPerson: rapaport@cs.buffalo.edu Remote host: adara.cs.buffalo.edu Remote ident: rapaport ### Begin Citation ### Do not delete this line ### %R 97-08 %A Ehrlich, Karen %A Rapaport, William J. %T A Computational Theory of Vocabulary Expansion %D May 5, 1997 %I Department of Computer Science, SUNY Buffalo %K computational linguistics, natural language learning, semantic networks, knowledge representation %Y As part of an interdisciplinary project to develop a computational cognitive model of a reader of narrative text, we are developing a computational theory of how natural-language-understanding systems can automatically expand their vocabulary by determining from context the meaning of words that are unknown, misunderstood, or used in a new sense. `Context' includes surrounding text, grammatical information, and background knowledge, but no external sources. Our thesis is that the meaning of such a word *can* be determined from context, can be *revised* upon further encounters with the word, ``*converges*'' to a dictionary-like definition if enough context has been provided and there have been enough exposures to the word, and eventually ``*settles down*'' to a ``steady state'' that is always subject to revision upon further encounters with the word. The system is being implemented in the SNePS knowledge-representation and reasoning system. This document is a slightly modified version (containing the algorithms) of that which is to appear in _Proceedings of the 19th Annual Conference of the Cognitive Science Society (Stanford University)_ (Mahwah, NJ: Lawrence Erlbaum Associates). It is also _Technical Report 97-2_ (Buffalo: SUNY Buffalo Center for Cognitive Science). It is also availI.2.7;I.2.6;I.2.4 %X As part of an interdisciplinary project to develop a computational cognitive model of a reader of narrative text, we are developing a computational theory of how natural-language-understanding systems can automatically expand their vocabulary by determining from context the meaning of words that are unknown, misunderstood, or used in a new sense. `Context' includes surrounding text, grammatical information, and background knowledge, but no external sources. Our thesis is that the meaning of such a word *can* be determined from context, can be *revised* upon further encounters with the word, ``*converges*'' to a dictionary-like definition if enough context has been provided and there have been enough exposures to the word, and eventually ``*settles down*'' to a ``steady state'' that is always subject to revision upon further encounters with the word. The system is being implemented in the SNePS knowledge-representation and reasoning system. This document is a slightly modified version (containing the algorithms) of that which is to appear in _Proceedings of the 19th Annual Conference of the Cognitive Science Society (Stanford University)_ (Mahwah, NJ: Lawrence Erlbaum Associates). It is also _Technical Report 97-2_ (Buffalo: SUNY Buffalo Center for Cognitive Science). It is also available on-line at .