A hybrid approach for paraphrase identification based on knowledge-enriched semantic heuristics

Author:

Mohamed Muhidin,Oussalah Mourad

Publisher

Springer Science and Business Media LLC

Subject

Library and Information Sciences,Linguistics and Language,Education,Language and Linguistics

Reference42 articles.

1. Alguliev, R., & Aliguliyev, R. (2009). Evolutionary algorithm for extractive text summarization. Intelligent Information Management, 1(02), 128.

2. Blacoe, W., & Lapata, M. (2012). A comparison of vector-based representations for semantic composition. In Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning: Association for computational linguistics (pp. 546–556).

3. Cilibrasi, R. L., & Vitanyi, P. M. (2007). The google similarity distance. IEEE Transactions on Knowledge and Data Engineering, 19(3), 370–83.

4. Das, D., & Smith, N. A. (2009). Paraphrase identification as probabilistic quasi-synchronous recognition. In Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP: Association for computational linguistics (Vol. 1, pp. 468–476).

5. Dias, G., Moraliyski, R., Cordeiro, J., Doucet, A., & Ahonen-Myka, H. (2010). Automatic discovery of word semantic relations using paraphrase alignment and distributional lexical semantics analysis. Natural Language Engineering, 16(04), 439–67.

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