Studies A GENERAL PURPOSE SEMANTIC PARSER USING FRAMENET AND WORDNET

Syntactic parsing is one of the best understood language processing applications. Since language and grammar have been formally defined, it is easy for computers to parse the syntactic structure of natural language text. Does meaning have structure as well? If it has, how can we analyze the structure? Previous systems rely on a one-to-one correspondence between syntactic rules and semantic rules. But such systems can only be applied to limited fragments of English. In this thesis, we propose a general-purpose shallow semantic parser which utilizes a semantic network (WordNet), and a frame dataset (FrameNet). Semantic relations recognized by the parser are based on how human beings represent knowledge of the world. Parsing semantic structure allows semantic units and constituents to be accessed and processed in a more meaningful way than syntactic parsing, moving the automation of understanding natural language text to a higher level. ACKNOWLEDGEMENTS I would like to acknowledge all those who helped make this thesis a reality. In particu-lar, I would like to acknowledge Dr. Rada Mihalcea for her invaluable advice, support and guidance, which are very important to the thesis. I would like to acknowledge Dr. Paul Tarau and other members in NLP group for sharing their knowledge and interesting ideas in various meetings and conversations, Dr. Kathy Swigger for teach-ing me “Natural Language Processing ” and “Artificial Intelligence”. I would also like to acknowledge my parents and friends for their emotional support and concern. i