Abstract
Statistical model have been used quite successfully in Natural Language Processing for recovery of hidden structure such as part-of-speech tags, or syntactic structure. This thesis considers semantic parsing and tagging of classical Chinese poetry lines. There are five aims in this thesis: (1) Construct semantic grammars; (2) Modify and learning probabilities of the semantic grammars from the training corpus; (3) Parse the sentence to tree structure; (4) Evaluate the accuracy of parsing results and (5) Compare with the Hidden Markov Model bi-gram tagger. In the first three tasks, we assumed that the categories of Chinese Thesaurus are representative enough to help us analyze the semantic of the sentences. And the semantic grammars were built upon the semantic categories and semantic rules. We modified the grammars and learned the probabilities from training data with Inside-Outside algorithm. And Viterbi algorithm was used to find the most likely parsing route. In the last two tasks, we found that the PCFG semantic parser has better performance on prediction of semantic tagging in the situation of data sparseness and the greater ability on disambiguation. We believe that parsing results might have broadly usages in machine translation, and poetry generation, and etc. in the future.