A method for discovering knowledge in texts

Abstract

We discuss a more powerful probabilistic graphical model for discovering semantic patterns from sequential text data, such as sentences. It is developed based on the idea that each word (or each symbol) in a sentence itself might carry lexical, semantic, or syntactic information, which can be used to replace conditional dependences in existing methods. Hence, our method has fewer conditional independence assumptions in contrast to these existing probabilistic graphical methods, such as CRFs, HMMs, MEMMs and Naive Bayes. Moreover, our method does not need to employ dynamic programming and therefore the on-line time complexity and memory complexity are reduced. We test the method on discovering noun phrases, the meaning of an ambiguous word, and semantic arguments of a verb in a sentence. We find that the misclassification rate is smaller compared to previously published results on the same data sets. For example, the method achieves an average f-measure of 98.25% for recognizing noun phrases on WSJ data from Penn Treebank; an average accuracy of 81.12% for recognizing the six sense word line; an average f-measure of 93.61% for classifying semantic argument boundaries of a verb in a sentence on WSJ data from Penn Treebank and PropBank.