| 基于最大熵模型的英文名词短语指代消解 |
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| 引用本文： | 钱伟,郭以昆,周雅倩,吴立德. 基于最大熵模型的英文名词短语指代消解[J]. 计算机研究与发展, 2003, 40(9): 1337-1343 |
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| 作者姓名： | 钱伟 郭以昆 周雅倩 吴立德 |
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| 作者单位： | 复旦大学计算机科学与工程系,上海,200433 |
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| 基金项目： | 国家自然科学基金(69873011,60103014);国家"八六三"高技术研究发展计划基金(2001AA114120) |
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| 摘 要： | 提出了一种新颖的基于语料库的英文名词短语指代消解算法,该算法不仅能解决传统的代词和名词／名词短语间的指代问题,还能解决名词短语间的指代问题。同时,利用最大熵模型,可以有效地综合各种互不相关的特征,算法在MUC7公开测试语料上F值达到了60．2％,极为接近文献记载的该语料库上F值的最优结果61．8％。
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| 关 键 词： | 最大熵 名词短语指代消解 自然语言处理 |
English Noun Phrase Coreference Resolution via a Maximum Entropy Model |
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| QIAN Wei,GUO Yi Kun,ZHOU Ya Qian,and WU Li De. English Noun Phrase Coreference Resolution via a Maximum Entropy Model[J]. Journal of Computer Research and Development, 2003, 40(9): 1337-1343 |
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| Authors: | QIAN Wei GUO Yi Kun ZHOU Ya Qian and WU Li De |
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| Abstract: | In this paper, a novel corpus based learning approach to noun phrase coreference resolution is presented This approach aims to solve not only pronoun anaphora problem, but also a more general noun phrase coreference one, which is introduced by MUC By applying the maximum entropy (M E ) model and utilizing a flexible object based architecture, the system is able to make use of a range of knowledge sources in training the classifier and achieves an F measure of 60 2%, which is very close to the state of art result (61 8%), on the MUC 7 coreference resolution task corpus |
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| Keywords: | maximum entropy noun phrase coreference resolution natural language processing |
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