自然言語の構文・意味解析規則の主観的確率を用いた帰納的学習システム

概要

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Natural language has many ambiguities, so the consideration of ambiguities is important for learning syntactic construction and semantics. In order to acquire the grammar and the meaning of words, it is necessary to have the flexibility and to gain the experience of learning by introducing not truth values (0 and 1) but a degree of uncertainty. So, for the purpose of modeling such a learning process, we introduced Dempster-Shafer's theory into our inductive learning system of natural language grammar. Our system, using CFG (Context-Free Grammar) as the syntactic representation and the semantic network as the semantic representation, can learn the grammar and the meaning of words with the epistemic (subjective) probability inductively, initially without presenting the grammar and the meaning of words.
1988-11-20