Recognition of textual entailment based on multi-feature

被引：0

作者：

Tan, Yong-Mei ^{[1
]}

Wang, Zhi-Hao ^{[1
]}

机构：

[1] School of Computer Science, Beijing University of Posts and Telecommunications, Beijing

来源：

Beijing Youdian Daxue Xuebao/Journal of Beijing University of Posts and Telecommunications | 2015年 / 38卷 / 06期

关键词：

Bayesian logistic regression; Multi-feature; Recognizing textual entailment;

D O I：

10.13190/j.jbupt.2015.06.020

中图分类号：

学科分类号：

摘要：

Recognizing textual entailment is an effective approach for computer to automatically identify semantic relation between texts with an important position in the field of natural language processing. A method using multi-feature was proposed. The new algorithm preprocess the raw text, Chinese characters segmentation, part-of-speech tagging, named entity recognition and dependency parser, string features, syntactic features and semantic features and uses Bayesian logistic regression model to predict the preliminary results, finally it uses the rules to filter the results. Experiments indicate that the algorithm's MacroF1 on RITE-VAL data is 0.625, outperform optimal value (MacroF1: 0.615, BUPTTeam-CS-SVBC-05). © 2015, Beijing University of Posts and Telecommunications. All right reserved.

引用

页码：98 / 103

页数：5

共 5 条

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[4]

Tan Y., Wang M., Et al., Recognizing textual entailment using multiple features, pp. 240-243, (2014)

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