Event graph based contradiction recognition from big data collection

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• 作者：Maofu Liu^a ; ^{liumaofu@wust.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Limin Wang^a ; ^{smile_wlm@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Liqiang Nie^b ; ^{nieliqiang@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jianhua Dai^c ; ^{david.joshua@qq.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Donghong Ji^d ; ^{dhji@whu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Contradiction recognition ; Event graph ; Big data ; High-level event semantic feature ; Atomic event ; Event semantic role
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：12 March 2016
• 年：2016
• 卷：181
• 期：Complete
• 页码：64-75
• 全文大小：1657 K

文摘

Conventional models relying on similarity utilizing low-level surface statistical, syntactic and lexical semantic features are suboptimal in contradiction recognition, especially for the large-scale data, such as the sensor and traffic data. To tackle this problem, this work treats the text and hypothesis pair as event graph and proposes a novel model based on event graph to recognize contradiction in big data collection. The proposed model is capable of seamlessly sewing the high-level event semantic features corresponding to the conflicting linguistic phenomena to identify contradictory construction. Experimental results show that our event graph based contradiction recognition model achieves significant improvement as compared to state-of-the-art competitors.