ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures

This paper presents our approach used for cross-lingual textual entailment task (task 8) organized within SemEval 2013. Crosslingual textual entailment (CLTE) tries to detect the entailment relationship between two text fragments in different languages. We solved this problem in three steps. Firstly, we use a off-the-shelf machine translation (MT) tool to convert the two input texts into the same language. Then after performing a text preprocessing, we extract multiple feature types with respect to surface text and grammar. We also propose novel feature types regarding to sentence difference and semantic similarity based on our observations in the preliminary experiments. Finally, we adopt a multiclass SVM algorithm for classification. The results on the cross-lingual data collections provided by SemEval 2013 show that (1) we can build portable and effective systems across languages using MT and multiple effective features; (2) our systems achieve the best results among the participants on two test datasets, i.e., FRA-ENG and DEU-ENG.