Syntactic Parsing for Mandarin Chinese via Structural Probability Re-estimation
| Autor: | Hsieh, Yu-Ming, 謝佑明 |
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| Rok vydání: | 2015 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 103 Syntactic parsing is the first major step of natural language understanding. It plays an important role in machine translation, question answering, information retrieval, speech recognition, and other natural language processing applications. Given a sentence and grammar rules, a syntactic parser may identify the part-of-speeches of words, then produce several ambiguous structures accepted by the grammar rules. However, to select the best structure from several ambiguous structures is a challenging task. Quality of the best structure selection usually depends on the precision of the structure probability estimation methods. In this thesis we first propose a general model, a context-dependent probability re-estimation model, to enhance the estimation of structure probabilities produced by probabilistic context-free grammars (PCFG). Compared with using rule probabilities only, the proposed model has the advantage of using effective, flexible, and broader range of contexture features to better estimate structure probabilities. Secondly we propose using specific models to resolve specific cases in parsing Chinese by pinpointing features specifically useful for such cases to enhance general models. The specific cases tested in this thesis are Vt-N structures and conjunctive structures. Evaluation on a set of experiments shows that the proposed models outperform the baseline parser and the existing state-of-the-art statistical parsers. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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