A Computational Theory of Vocabulary Acquisition
نویسندگان
چکیده
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 acquire new 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 knowledgerepresentation and reasoning system. This essay has been published as a chapter in Iwańska, Łucja, & Shapiro, Stuart C. (2000), Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language (Menlo Park, CA/Cambridge, MA: AAAI Press/MIT Press): 347–375. All citations should be to the published version. The present version is Technical Report 98-05 (Buffalo: SUNY Buffalo Department of Computer Science) and Technical Report 98-1 (Buffalo: SUNY Buffalo Center for Cognitive Science). Unlike the published version, this one has only minimal typographical errors, whereas the published version is maximal in that regard; errata for the published version are available at [http://www.cse.buffalo.edu/∼rapaport/papers.html#krnlp].
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تاریخ انتشار 1998