Developing Probabilistic Models for Identifying Semantic Patterns in Texts

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 Tree bank and Prop Bank, 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 Tree bank.