A Study of Dynamic Word Sense Disambiguation Based on Full-sentence Co-occurrence of Node Word
Based on the property that word sense disambiguation is the return of word sense to context,we propose a dynamic word sense disambiguation method based on full-sentence co-occur-rence of node word.The method firstly uses the full sentence as a window to limit the node word us-age context,secondly uses statistical methods such as mutual information,chi-square test and ratio of relative word rank to extract semantically related words,and builds a related semantic category data-base by referring to"Tongyici Cilin"(A Dictionary of Synonyms),and finally uses the co-occurrence frequency as a weighting factor to disambiguate the low and medium frequency co-occurring multi-sense words by relying on the distribution rate of single-sense word meaning clusters.The method is used to disambiguate 1030 multiple-meaning words with less than 7 meaning categories that co-oc-curred with"meili"(beautiful),and a correct rate of 85.2％is achieved in the test.
node wordwhole sentence co-occurrenceword sense disambiguationsemantic cluste-ringunsupervised learning
万方数据
维普
