Combining Pseudo-Samples and Manually-Tagged Samples for Word Sense Disambiguation
Teng Hong-fei · Zhongwen xinxi xuebao · 2009
The corpus-based method for word sense disambiguation(WSD) suffers from knowledge acquisition bottleneck problem.The automatic lexical sample acquisition method based on equivalent pseudo-words(EPs) is an effective way to solve of this problem.However,some pseudo-samples collected by EPs have low quality and the EPs can not be acquired when the ambiguous word has few monosemous synonyms.This paper proposes a WSD method combining pseudo-samples and man-acquired samples.The method calculates the sentence similarity with the context of the ambiguous word to remove pseudo-samples with low quality.Moreover,the method utilizes the manually-tagged corpus to get the sense distribution probability and provide samples for the ambiguous words that have little monosemous synonym.Our method achieves an average F-measure of 0.79 through the WSD experiments performed on Senseval-3 Chinese lexical sample task.