篇名 | Improve Parsing Performance by Self-Learning |
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卷期 | 12:2 |
作者 | Hsieh, Yu-ming 、 Yang, Duen-chi 、 Chen, Keh-jiann |
頁次 | 195-216 |
關鍵字 | Parsing 、 Semantic 、 PoS Tagging 、 PCFG 、 Knowledge Extraction 、 Word association 、 THCI Core |
出刊日期 | 200706 |
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 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.