• Phonetics and Speech Sciences
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• v.1 no.2
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• pp.63-73
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• 2009

The present study was conducted to explore how the processing of lexical ambiguity between homonymy and polysemy differs from each other, and whether the representation of mental lexicon was separated from each lexical ambiguity by a semantic priming paradigm. Homonymy (M1 means the literal meaning of '사과', i.e. apple and M2 means another literal meaning of '사과', i.e. apologize) was used in Experiment I, and polysemy (M2 means the literal meaning of '바람', i.e. wind and M2 means the figurative meaning of '바람', i.e. wanton) was used in Experiment 2. The results of both experiments showed that a significant semantic priming effect occurs regardless of the type of ambiguities (homonymy and polysemy) and the difference of their semantic processes. However, the semantic priming effect for polysemy was larger than that for homonymy. This result supports the hypothesis that the semantic process of homonymy is different from that of polysemy.

• Journal of KIISE:Software and Applications
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• v.28 no.5
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• pp.448-448
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• 2001

The definitions in Korean dictionary not only describe meanings of title, but also include various semantic information such as hypernymy/hyponymy, meronymy/holonymy, polysemy, homonymy, synonymy, antonymy, and semantic features. This paper purposes to implement a parser as the basic tool to acquire automatically the semantic information from the definitions in Korean dictionary. For this purpose, first we constructed the part-of-speech tagged corpus and the tree tagged corpus from the definitions in Korean dictionary. And then we automatically extracted from the corpora the frequency of words which are ambiguous in part-of-speech tag and the grammar rules and their probability based on the statistical method. The parser is a kind of the probabilistic chart parser that uses the extracted data. The frequency of words which are ambiguous in part-of-speech tag and the grammar rules and their probability resolve the noun phrase's structural ambiguity during parsing. The parser uses a grammar factoring, Best-First search, and Viterbi search In order to reduce the number of nodes during parsing and to increase the performance. We experiment with grammar rule's probability, left-to-right parsing, and left-first search. By the experiments, when the parser uses grammar rule's probability and left-first search simultaneously, the result of parsing is most accurate and the recall is 51.74% and the precision is 87.47% on raw corpus.