TY - GEN
T1 - Recognizing entailment in Chinese texts with feature combination
AU - Liu, Maofu
AU - Guo, Yifan
AU - Nie, Liqiang
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/4/12
Y1 - 2016/4/12
N2 - In recent years, the natural language processing community has been manifesting increasing interest in textual entailment recognition among English texts. Yet, so far, not much attention has been paid to textual entailment recognition in Chinese texts. Recognizing entailment can be cast as a classification problem, and in this paper, a classification model based on support vector machine is constructed to detect semantic relations in Chinese text pair, including forward entailment, reverse entailment, bidirectional entailment, contradiction and independence for the multi-class task. We introduce different feature combinations based on four kinds of features, containing Chinese surface textual, Chinese lexical semantic, Chinese syntactic and Chinese linguistic phenomena features, to our classification model. The experimental results on NTCIR RITE-3 data collection show that the accuracy of our classification model using the feature combination with all the four kinds of Chinese textual features achieves a much better performance than all other systems on multi-class task.
AB - In recent years, the natural language processing community has been manifesting increasing interest in textual entailment recognition among English texts. Yet, so far, not much attention has been paid to textual entailment recognition in Chinese texts. Recognizing entailment can be cast as a classification problem, and in this paper, a classification model based on support vector machine is constructed to detect semantic relations in Chinese text pair, including forward entailment, reverse entailment, bidirectional entailment, contradiction and independence for the multi-class task. We introduce different feature combinations based on four kinds of features, containing Chinese surface textual, Chinese lexical semantic, Chinese syntactic and Chinese linguistic phenomena features, to our classification model. The experimental results on NTCIR RITE-3 data collection show that the accuracy of our classification model using the feature combination with all the four kinds of Chinese textual features achieves a much better performance than all other systems on multi-class task.
KW - Chinese lexical semantic feature
KW - Chinese linguistic phenomina feature
KW - Chinese surface textual feature
KW - Chinese syntactic feature
KW - Chinese textual entailment
KW - feature combination
KW - support vector machine
UR - https://www.scopus.com/pages/publications/84970016797
U2 - 10.1109/IALP.2015.7451537
DO - 10.1109/IALP.2015.7451537
M3 - 会议稿件
AN - SCOPUS:84970016797
T3 - Proceedings of 2015 International Conference on Asian Language Processing, IALP 2015
SP - 82
EP - 85
BT - Proceedings of 2015 International Conference on Asian Language Processing, IALP 2015
A2 - Ma, Bin
A2 - Zhang, Min
A2 - Lu, Yanfeng
A2 - Dong, Minghui
A2 - Chen, Wenliang
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - International Conference on Asian Language Processing, IALP 2015
Y2 - 24 October 2015 through 25 October 2015
ER -