NDLI: Recognizing cross-lingual textual entailment with co-training using similarity and difference views

Content Provider	IEEE Xplore Digital Library
Author	Jiang Zhao Man Lan Zheng-Yu Niu Donghong Ji
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci. & Technol., East China Normal Univ., Shanghai, China (Jiang Zhao; Man Lan) \|\| Sch. of Comput. Sci., Wuhan Univ., Wuhan, China (Donghong Ji) \|\| Baidu Inc., Beijing, China (Zheng-Yu Niu)
Abstract	Cross-lingual textual entailment is a relatively new problem that detects the entailment relationship between two text fragments written in different languages. Previous work adopted machine learning algorithms and similarity measures as features to address this task. In order to overcome the high cost of human annotation and further improve the recognition performance, we present a novel co-training approach to solve this problem. We first use an off-the-shelf machine translation tool to eliminate the language gap between two texts. Then we measure the similarities and differences between two texts and regard them as sufficient and redundant views. We use those two views to conduct the co-training procedure to perform classification. Besides, a new effective Kullback-Leibler (KL) based criterion is proposed to select the results from all possible iterations. Experiments on cross-lingual datasets provided by SemEval 2013 show that our method significantly outperforms the baseline systems and previous work.
Starting Page	3705
Ending Page	3712
File Size	402183
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889713
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Feature extraction Support vector machines Semantics Accuracy Learning systems Prediction algorithms
Content Type	Text
Resource Type	Article

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