Abstract
Learning how to parse a sentence has been a challenging problem in natural language acquisition. Traditional supervised parser learning methods normally had given very strong assumptions on the preparation of the correct parsed training data that makes the acquisition of a large set of well trained corpora a big burden on trainers and thus often makes the parser-learning problem becomes infeasible.Reinforcement learning (RL) is a very powerful learning technique in that only rewards are needed to give in a successful sequence of actions and thus it requires less requirements on the trainers than traditional supervised learning methods. This feature is less addressed in traditional Natural language parser learning methods. In this thesis we show that it is suitable to apply RL if we adopt the proper data structures. The effectiveness and robustness on learning a parser using RL are also the research foci in this thesis. In particular, we emphasize on the strategies of reward giving schema in RL and discuss their corresponding performances on the trained parsers given different rewarding schema. In this dissertation, we proposed two kinds of rewarding schema and compared their advantages and disadvantages by experiments on the learning of Chinese sentence phrase structures. The first one is called intermediate-route rewarding (IRR), and the second one is called delayed partial rewarding (DPR). IRR schema gives the parser reward when it achieves the state that will be traversed if correct actions are conducted. And the parser will be punished when arriving other states. DPR gives the parser rewards only when it finishes a correct sub-parse (or a phrase). Under other states, the parser will not be rewarded or punished. Comparing the performance of these two rewarding schemata, IRR rewarding schema outperforms the DPR schema in F-score. For the coverage, DPR performs better. We will make the discussion in detail in Chapter 4.