Word segmentation refinement by Wikipedia for textual entailment

Chuan-Jie Lin, Yu-Cheng Tu
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引用次数: 2

Abstract

Textual entailment in Chinese differs from the way handling English because of the lack of word delimiters and capitalization. Information from word segmentation and Wikipedia often plays an important role in textual entailment recognition. However, the inconsistency of boundaries of word segmentation and matched Wikipedia titles should be resolved first. This paper proposed 4 ways to incorporate Wikipedia title matching and word segmentation, experimented in several feature combinations. The best system redoes word segmentation after matching Wikipedia titles. The best feature combination for BC task uses content words and Wikipedia titles only, which achieves a macro-average F-measure of 67.33% and an accuracy of 68.9%. The best MC RITE system also achieves a macro-average F-measure of 46.11% and an accuracy of 58.34%. They beat all the runs in NTCIR-10 RITE-2 CT tasks.
维基百科对文本蕴涵的分词改进
汉语的文本蕴涵不同于英语的处理方式,因为汉语没有分隔词和大小写。来自分词和维基百科的信息在文本蕴涵识别中起着重要的作用。但首先要解决的是分词边界与维基百科标题匹配不一致的问题。本文提出了4种结合维基百科标题匹配和分词的方法,并对几种特征组合进行了实验。最好的系统在匹配维基百科标题后重新分词。BC任务的最佳特征组合仅使用内容词和维基百科标题,其宏观平均f度量值为67.33%,准确率为68.9%。最佳MC - RITE系统的宏观平均f值为46.11%,精度为58.34%。他们打败了所有的ntir -10 RITE-2 CT任务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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