Abstract
Textual entailment has been proposed as a unifying generic framework for modeling language variability and semantic inference in different Natural Language Processing (NLP) tasks. By evaluating on NTCIR-11 RITE3 Simplified Chinese subtask data set, this paper firstly demonstrates and compares the performance of Chinese textual entailment recognition models that combine different lexical, syntactic, and semantic features. Then a word embedding based lexical entailment module is added to enhance classification ability of our system further. The experimental results show that the word embedding for lexical semantic relation reasoning is effective and efficient in Chinese textual entailment.
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Acknowledgements
This work is supported by grants from National Natural Science Foundation of China (No. 61163039, No. 61163036, No. 61363058) and the Young Teacher Research Ability Enhancement Project of Northwest Normal University of China (NWNU-LKQN-10-2, NWNU-LKQN-13-23).
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Zhang, Z., Yao, D., Pang, Y., Lu, X. (2015). Chinese Textual Entailment Recognition Enhanced with Word Embedding. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_8
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