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基于最大熵模型的英文名词短语指代消解
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.
作者姓名:钱伟  郭以昆  周雅倩  吴立德
作者单位:复旦大学计算机科学与工程系,上海,200433
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)
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%。

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
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.
Authors:QIAN Wei  GUO Yi Kun  ZHOU Ya Qian  and WU Li De
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
Keywords:maximum entropy  noun phrase coreference resolution  natural language processing  
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