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基于最大熵模型的英文名词短语指代消解
English Noun Phrase Coreference Resolution via a Maximum Entropy Model
【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MUC 7公开测试语料上F值达到了 6 0 2 % ,极为接近文献记载的该语料库上F值的最优结果 6 1 8%
【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
【Key words】 maximum entropy; noun phrase coreference resolution; natural language processing;
- 【文献出处】 计算机研究与发展 ,Journal of Computer Research and Development , 编辑部邮箱 ,2003年09期
- 【分类号】TP391.1
- 【被引频次】44
- 【下载频次】356