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All issues Volume 8 (2016) ITM Web Conf., 8 (2016) 01006 References
Open Access
Issue
ITM Web Conf.
Volume 8, 2016
International Conference on Big Data and its Applications (ICBDA 2016)
Article Number 01006
Number of page(s) 8
DOI https://doi.org/10.1051/itmconf/20160801006
Published online 22 November 2016
  1. M.A. Hearst. Automatic Acquisition of Hyponyms from Large Text Corpora. Proceedings of the 14th Conference on Computational Linguistics - Volume 2. COLING ’92, Association for Computational Linguistics (1992)539–545 [Google Scholar]
  2. T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado, J. Dean. Distributed Representations of Words and Phrases and their Compositionality. Advances in Neural Information Processing Systems 26. Curran Associates, Inc. (2013) 3111–3119 [Google Scholar]
  3. A. Panchenko, S. Faralli, E. Ruppert, S. Remus, H. Naets, C. Fairon, S.P. Ponzetto, C. Biemann. TAXI at SemEval-2016 Task 13 a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling. Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). (2016) 1320–1327 [Google Scholar]
  4. R. Fu, J. Guo, B. Qin, W. Che, H. Wang, T. Liu. Learning Semantic Hierarchies via Word Embeddings. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1 Long Papers), Association for Computational Linguistics (2014) 1199–1209 [Google Scholar]
  5. O. Levy, S. Remus, C. Biemann, I. Dagan. Do Supervised Distributional Methods Really Learn Lexical Inference Relations? Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics Human Language Technologies, Denver, Colorado, Association for Computational Linguistics (2015) 970–976 [Google Scholar]
  6. A. Kutuzov, M. Kopotev, T. Sviridenko, L. Ivanova. Clustering Comparable Corpora of Russian and Ukrainian Academic Texts Word Embeddings and Semantic Fingerprints. Proceedings of the Ninth Workshop on Building and Using Comparable Corpora. (2016)3–10 [Google Scholar]
  7. T. Mikolov, Q.V. Le, I. Sutskever. Exploiting Similarities among Languages for Machine Translation. CoRR abs/1309.4168 (2013) [Google Scholar]
  8. I. Vulić, A. Korhonen. On the Role of Seed Lexicons in Learning Bilingual Word Embeddings. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1 Long Papers), Berlin, Germany, Association for Computational Linguistics (2016) 247–257 [Google Scholar]
  9. E. Vylomova, L. Rimell, T. Cohn, T. Baldwin. Take and Took, Gaggle and Goose, Book and Read Evaluating the Utility of Vector Differences for Lexical Relation Learning. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1 Long Papers), Berlin, Germany, Association for Computational Linguistics (2016) 1671–1682 [Google Scholar]
  10. V. Shwartz, Y. Goldberg, I. Dagan. Improving Hypernymy Detection with an Integrated Path-based and Distributional Method. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1 Long Papers), Berlin, Germany, Association for Computational Linguistics (2016) 2389–2398 [Google Scholar]
  11. A. Handler. An empirical study of semantic similarity in WordNet and Word2Vec. Master’s thesis, University of New Orleans (2014) [Google Scholar]
  12. M. Abadi et al. TensorFlow Large-Scale Machine Learning on Heterogeneous Distributed Systems. CoRR abs/1603.04467 (2016) [Google Scholar]
  13. D.P. Kingma, J. Ba. Adam A Method for Stochastic Optimization. CoRR abs/1412.6980 (2014) [Google Scholar]
  14. N. Arefyev, A. Panchenko, A. Lukanin, O. Lesota, P. Romanov. Evaluating Three Corpus-Based Semantic Similarity Systems for Russian. Computational Linguistics and Intellectual Technologies Papers from the Annual International Conference “Dialogue”, Volume 2 of 2. Papers from special sessions, Moscow, RGGU (2015)106–118 [Google Scholar]
  15. A. Panchenko, O. Morozova, H. Naets. A Semantic Similarity Measure Based on Lexico-Syntactic Patterns. Proceedings of KONVENS 2012, ÖGAI (2012) 174–178 [Google Scholar]
  16. A.A. Krizhanovsky, A.V. Smirnov. An approach to automated construction of a general-purpose lexical ontology based on Wiktionary. Journal of Computer and Systems Sciences International 52 (2)(2013) 215–225 [CrossRef] [Google Scholar]
  17. P.J. Rousseeuw. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20 (1987) 53–65 [CrossRef] [Google Scholar]

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