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Volumn , Issue , 2015, Pages 2044-2048

Specializing word embeddings for similarity or relatedness

Author keywords

[No Author keywords available]

Indexed keywords

EMBEDDINGS; INFORMATION RETRIEVAL SYSTEMS; SEMANTICS;

EID: 84959920093     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.18653/v1/d15-1242     Document Type: Conference Paper
Times cited : (138)

References (23)
  • 1
    • 84858390207 scopus 로고    scopus 로고
    • A study on similarity and relatedness using distributional and WordNet-based approaches
    • Eneko Agirre, Enrique Alfonseca, Keith B. Hall, Jana Kravalova, Marius Pasca, and Aitor Soroa. 2009. A study on similarity and relatedness using distributional and WordNet-based approaches. In NAACL, pages 19-27.
    • (2009) NAACL , pp. 19-27
    • Agirre, E.1    Alfonseca, E.2    Hall, K.B.3    Kravalova, J.4    Pasca, M.5    Soroa, A.6
  • 2
    • 84906930943 scopus 로고    scopus 로고
    • Don't count, predict! A systematic comparison of context-counting vs. Context-predicting semantic vectors
    • Marco Baroni, Georgiana Dinu, and German Kruszewski. 2014. Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors. In ACL, pages 238-247.
    • (2014) ACL , pp. 238-247
    • Baroni, M.1    Dinu, G.2    Kruszewski, G.3
  • 5
    • 84927942005 scopus 로고    scopus 로고
    • Vector space models of lexical meaning
    • Shalom Lappin and Chris Fox, editors, chapter 16. Wiley-Blackwell, Oxford
    • Stephen Clark. 2015. Vector Space Models of Lexical Meaning. In Shalom Lappin and Chris Fox, editors, Handbook of Contemporary Semantics, chapter 16. Wiley-Blackwell, Oxford.
    • (2015) Handbook of Contemporary Semantics
    • Clark, S.1
  • 9
    • 0000679216 scopus 로고
    • Distributional structure
    • Zelig Harris. 1954. Distributional Structure. Word, 10(23):146-162.
    • (1954) Word , vol.10 , Issue.23 , pp. 146-162
    • Harris, Z.1
  • 10
    • 84959876599 scopus 로고    scopus 로고
    • Embedding word similarity with neural machine translation
    • 1412.6448
    • Felix Hill, Kyunghyun Cho, Sébastien Jean, Coline Devin, and Yoshua Bengio. 2014a. Embedding word similarity with neural machine translation. CoRR, abs/1412.6448.
    • (2014) CoRR
    • Hill, F.1    Cho, K.2    Jean, S.3    Devin, C.4    Bengio, Y.5
  • 11
    • 84943769028 scopus 로고    scopus 로고
    • SimLex-999: Evaluating semantic models with (genuine) similarity estimation
    • 1408.3456
    • Felix Hill, Roi Reichart, and Anna Korhonen. 2014b. SimLex-999: Evaluating semantic models with (genuine) similarity estimation. CoRR, abs/1408.3456.
    • (2014) CoRR
    • Hill, F.1    Reichart, R.2    Korhonen, A.3
  • 13
    • 0000600219 scopus 로고    scopus 로고
    • A solution to plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge
    • Thomas K Landauer and Susan T Dumais. 1997. A solution to plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological review, 104(2):211.
    • (1997) Psychological Review , vol.104 , Issue.2 , pp. 211
    • Landauer, T.K.1    Dumais, S.T.2
  • 15
    • 85083951332 scopus 로고    scopus 로고
    • Efficient estimation of word representations in vector space
    • Scottsdale, Arizona, USA
    • Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a. Efficient estimation of word representations in vector space. In Proceedings of ICLR, Scottsdale, Arizona, USA.
    • (2013) Proceedings of ICLR
    • Mikolov, T.1    Chen, K.2    Corrado, G.3    Dean, J.4
  • 16
    • 84899922637 scopus 로고    scopus 로고
    • Exploiting similarities among languages for machine translation
    • Scottsdale, Arizona, USA
    • Tomas Mikolov, Quoc V Le, and Ilya Sutskever. 2013b. Exploiting similarities among languages for machine translation. In Proceedings of ICLR, Scottsdale, Arizona, USA.
    • (2013) Proceedings of ICLR
    • Mikolov, T.1    Le, Q.V.2    Sutskever, I.3
  • 20
    • 80053495924 scopus 로고    scopus 로고
    • Word representations: A simple and general method for semi-supervised learning
    • Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010. Word representations: a simple and general method for semi-supervised learning. In Proceedings of ACL, pages 384-394.
    • (2010) Proceedings of ACL , pp. 384-394
    • Turian, J.1    Ratinov, L.2    Bengio, Y.3
  • 21
    • 77952700189 scopus 로고    scopus 로고
    • From Frequency to Meaning: Vector space models of semantics
    • January
    • Peter D. Turney and Patrick Pantel. 2010. From Frequency to Meaning: vector space models of semantics. Journal of Artifical Intelligence Research, 37(1):141-188, January.
    • (2010) Journal of Artifical Intelligence Research , vol.37 , Issue.1 , pp. 141-188
    • Turney, P.D.1    Pantel, P.2
  • 22
    • 33748661515 scopus 로고    scopus 로고
    • Similarity of semantic relations
    • Peter D. Turney. 2006. Similarity of semantic relations. Computational Linguistics, 32(3):379-416.
    • (2006) Computational Linguistics , vol.32 , Issue.3 , pp. 379-416
    • Turney, P.D.1
  • 23
    • 58149411184 scopus 로고
    • Features of similarity
    • Amos Tversky. 1977. Features of similarity. Psychological Review, 84 (4).
    • (1977) Psychological Review , vol.84 , Issue.4
    • Tversky, A.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.