Supervised Learning to Measure the Semantic Similarity Between Arabic Sentences-Reference-Cited by-同舟云学术

Supervised Learning to Measure the Semantic Similarity Between Arabic Sentences

Published:2015 Issue: Volume: Page:158-167
ISSN:0302-9743
Container-title:Computational Collective Intelligence
language:
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Author:

Wali Wafa,Gargouri Bilel,hamadou Abdelmajid Ben

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-24069-5_15

Reference15 articles.

1. Yuhua, L., McLean, D., Bandar, Z.A., O’Shea, J.D., Crockett, K.: Sentence similarity based on semantic nets and corpus statistics. In: IEEE Transactions on Knowledge and Data Engineering, vol. 18, pp. 1138–1150 (2006)

2. Lee, M., Chang, J., Hsieh, T.: A Grammar-Based Semantic Similarity Algorithm for Natural Language Sentences. The Scientific World Journal 2014, Article ID 437162, 17 (2014). http://dx.doi.org/10.1155/2014/43716

3. Lecture Notes in Computer Science;D Ştefănescu,2014

4. Almarsoomi, F., O’Shea, J., Bandar, Z, Crockett, K.: Arabic word semantic similarity. In: World Academy of Science, Engineering and Technology (WASET 2012), vol. 6, pp. 81–89 (2012)

5. Francopoulo, G., George, M.: Language Resource Management. 2008. Lexical Markup Framework (LMF). Technical report, ISO/TC 37/SC 4 N453 (N330 Rev.16) (2008)

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