頁籤選單縮合
題 名 | Improve Parsing Performance by Self-Learning |
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作 者 | Hsieh, Yu-ming; Yang, Duen-chi; Chen, Keh-jiann; | 書刊名 | International Journal of Computational Linguistics & Chinese Language Processing |
卷 期 | 12:2 2007.06[民96.06] |
頁 次 | 頁195-216 |
分類號 | 312.13 |
關鍵詞 | Parsing; Word association; Knowledge extraction; PCFG; PoS tagging; Semantic; |
語 文 | 英文(English) |
英文摘要 | There are many methods to improve performance of statistical parsers. Resolving structural ambiguities is a major task of these methods. In the proposed approach, the parser produces a set of n-best trees based on a feature-extended PCFG grammar and then selects the best tree structure based on association strengths of dependency word-pairs. However, there is no sufficiently large Treebank producing reliable statistical distributions of all word-pairs. This paper aims to provide a self-learning method to resolve the problems. The word association strengths were automatically extracted and learned by parsing a giga-word corpus. Although the automatically learned word associations were not perfect, the constructed structure evaluation model improved the bracketed f-score from 83.09% to 86.59%. We believe that the above iterative learning processes can improve parsing performances automatically by learning word-dependence information continuously from web. |
本系統中英文摘要資訊取自各篇刊載內容。