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author = Dagan, Ido (11 entries)  Select: All None   Action: Show BibTeX

Oren Melamud, Ido Dagan, Jacob Goldberger, Idan Szpektor and Deniz Yuret. 2014. Probabilistic Modeling of Joint-context in Distributional Similarity. In Proceedings of the Eighteenth Conference on Computational Natural Language Learning, pp 181--190, Ann Arbor, Michigan, June. Association for Computational Linguistics. [ai.ku, scode] url pdf google scholar
Lili Kotlerman, Ido Dagan, Idan Szpektor and Maayan Zhitomirsky-Geffet. 2010. Directional distributional similarity for lexical inference. Natural Language Engineering, vol 16, no 4, pp 359--389. Cambridge Univ Press. [ddsim, fulbright] pdf pdf google scholar
Maayan Geffet and Ido Dagan. 2005. The distributional inclusion hypothesis. In Proceedings of the 43rd annual meeting of the Association for Computational Linguistics (ACL2005). [Paraphrase] pdf google scholar
Ido Dagan, Lillian Lee and Fernando CN Pereira. 1998. Similarity-based models of word cooccurance probabilities. [NLP] google scholar
Ido Dagan, Lillian Lee and Fernando C. N. Pereira. 1998. Similarity-based models of word cooccurance probabilities. [WSD] google scholar
Ido Dagan, Lillian Lee and Fernando Pereira. 1997. Similarity-based methods for word sense disambiguation. att;cat. [WSD] google scholar
Ido Dagan, Lillian Lee and Fernando Pereira. 1997. Similarity-based methods for word sense disambiguation. [NLP] google scholar
Ido Dagan, Fernando Pereira and Lillian Lee. 1994. Similarity-based estimation of word cooccurance probabilities. att;cat. [WSD] google scholar
Ido Dagan, Fernando Pereira and Lillian Lee. 1994. Similarity-based estimation of word cooccurance probabilities. [NLP] google scholar
Ido Dagan, S. Markus and S. Markovitch. 1993. Contextual word similarity and estimation from sparse data. In Proceedings of the 31st Annual Meeting of the Association for Computational Linguist, pp 164-171. att;cat;HLTsurvey. [WSD] google scholar
Ido Dagan, S. Markus and S. Markovitch. 1993. Contextual word similarity and estimation from sparse data. In Proceedings of the 31st Annual Meeting of the Association for Computational Linguist, pp 164-171. [NLP] google scholar

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