Corpus-driven Linguistic Approaches to Sense Prediction
Date Issued
2010
Date
2010
Author(s)
Hong, Jia-Fei
Abstract
In this study, I proposed using corpus-driven distribution as the main method of prediction. I concentrated on individual semantic features to predict the senses of non-defined words by using corpora and tools, such as Chinese Gigaword Corpus, HowNet, Chinese Wordnet, and XianDai HanYu CiDian (Xian Han). Using these corpora, I determined the collocation clusters of the four target words--- chi1 “eat”, wan2 “play”, huan4 “change” and shao1 “burn” through character similarities and concepts similarities.
The four target words are all transitive verbs and they each have more than two senses. The collocation words of the four target words are very useful and play an important role in this sense prediction study. When conducting the character similarity clustering analysis, I employed identical morphemes of some of the collocation words in order to cluster them into the same cluster. Therefore, there are two main strategies of the corpus-based and computational approach used in this sense prediction study: (1) character similarity clustering analysis; and (2) concept similarity clustering analysis, which encompasses via HowNet (a) similarity between sememes, and (b) similarity between concepts. In this sense prediction study, I first predicted that different clusters can represent different senses, and I examined the accuracy rates of the four target words via the character similarity clustering analysis and the concept similarity clustering analysis of the corpus-based and computational approach. Then, I evaluated the four target words via sense divisions in Chinese Wordnet and in Xiandai Hanyu Cidian and was able to employ automatically computational programming to predict different senses for chi “eat”, wan2 “play”, huan4 “change”, and shao1 “burn”.
After the corpus-based and computational approach used in this sense prediction study, I demonstrated that I was able to use off-line tasks to test my participants’ intuition, which supports the theory that different clusters can represent different senses when using the corpus-based and computational approach. Therefore, in order to examine the related collocation words for the lexically ambiguous target words, I employed a multiple-choice task (Burton et al. 1991). In addition, because the stimuli were collected from the character similarity clustering analysis of the corpus-based and computational approach, I demonstrated the viability of this approach by the results presented in this sense prediction study.
Subjects
Lexical ambiguity
sense prediction
corpus-based approach
character similarity clustering approach
concept similarity clustering approach
experimental Evaluation
Type
thesis
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