Abstract
This paper introduces an approach combining transformation and classification methods for recognizing textual entailment. In transformation model, directional and undirected inference relations are recognized, and text fragments having such relations in text are replaced by the counterparts in hypothesis. In classification model, a hybrid kernel-based approach is introduced, and three kinds of features are employed for classifying entailment. Experimental results show that the combination approach achieves a better performance in comparison with the single classification system.
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Acknowledgements
This work is supported by Natural Science Foundation of Hainan (618MS086), Special innovation project of Guangdong Education Department (2017KTSCX064), Natural Science Foundation of China (61702121) and Bidding Project of GDUFS Laboratory of Language Engineering and Computing (LEC2016ZBKT001, LEC2016ZBKT002).
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Ren, H., Wan, J., Chen, X. (2019). Combining Transformation and Classification for Recognizing Textual Entailment. In: Peng, H., Deng, C., Wu, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2018. Communications in Computer and Information Science, vol 986. Springer, Singapore. https://doi.org/10.1007/978-981-13-6473-0_22
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DOI: https://doi.org/10.1007/978-981-13-6473-0_22
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