A Computational Theory of Vocabulary Acquisition (1998)
| Venue: | Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language (Menlo Park, CA/Cambridge |
| Citations: | 22 - 11 self |
BibTeX
@INPROCEEDINGS{Rapaport98acomputational,
author = {William J. Rapaport and Karen Ehrlich},
title = {A Computational Theory of Vocabulary Acquisition},
booktitle = {Natural Language Processing and Knowledge Representation: Language for Knowledge and Knowledge for Language (Menlo Park, CA/Cambridge},
year = {1998},
pages = {347--375},
publisher = {AAAI Press/MIT Press}
}
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Abstract
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 is forthcoming as a chapter in Iwanska, L/ucja, & S...
