Developing Probabilistic Models for Identifying Semantic Patterns in Texts (original) (raw)
2011, 2011 IEEE Fifth International Conference on Semantic Computing
We present a probabilistic graphical model that finds a sequence of optimal categories for a sequence of input symbols. Based on this mode, three algorithms are developed for identifying semantic patterns in texts. They are the algorithm for extracting semantic arguments of a verb, the algorithm for classifying the sense of an ambiguous word, and the algorithm for identifying noun phrases from a sentence. Experiments conducted on standard data sets show good results. For example, our method achieves an average precision of 92.96% and an average recall of 94.94% for extracting semantic argument boundaries of verbs on WSJ data from Penn Treebank and PropBank; an average accuracy of 81.12% for recognizing the six sense word line ; and an average precision of 97.7% and an average recall of 98.8% for recognizing noun phrases on WSJ data from Penn Treebank.
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