Part of
Computational Phraseology
Edited by Gloria Corpas Pastor and Jean-Pierre Colson
[IVITRA Research in Linguistics and Literature 24] 2020
► pp. 311324
References
Attia, M., Samih, Y., Faruqui, M., & Maier, W.
(2018) GHH at SemEval-2018 task 10: Discovering discriminative attributes in distributional semantics. In Proceedings of the 12th international workshop on semantic evaluation (pp. 947–952). New Orleans, LA: Association for Computational Linguistics. Retrieved from [URL]. DOI logoGoogle Scholar
Blevins, J. P.
(2016) Word and paradigm morphology. Oxford: Oxford University Press. DOI logoGoogle Scholar
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T.
(2016) Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606.Google Scholar
Church, K., & Hanks, P.
(1990) Word association norms, mutual information, and lexicography. Computational Linguistics, 16(1), 22–29.Google Scholar
Cortes, C., & Vapnik, V.
(1995) Support-vector networks. Machine Learning, 20(3), 273–297. DOI logoGoogle Scholar
Davis, E.
(1990) Representations of commonsense knowledge. San Francisco, CA: Morgan Kaufmann Publishers Inc.Google Scholar
Dice, L. R.
(1945) Measures of the amount of ecologic association between species. Ecology, 26, 297–302. DOI logoGoogle Scholar
Dunning, T.
(1993) Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1), 61–74.Google Scholar
Evert, S.
(2005) The statistics of word co-occurrences: word pairs and collocations. (PhD Thesis, University of Stuttgart, Stuttgart). Retrieved from [URL].Google Scholar
Fagarasan, L., Vecchi, E. M., & Clark, S.
(2015) From distributional semantics to feature norms: grounding semantic models in human perceptual data. In Proceedings of the 11th international conference on computational semantics (pp. 52–57).Google Scholar
Faruqui, M., Dodge, J., Jauhar, S. K., Dyer, C., Hovy, E., & Smith, N. A.
(2014) Retrofitting word vectors to semantic lexicons. arXiv preprint arXiv:1411.4166. DOI logoGoogle Scholar
Firth, J.
(1957 [1968]) A synopsis of linguistic theory, 1930–1955. In F. R. Palmer (Ed.), Selected Papers of J. R. Firth, (1952–59) (pp. 168–205). London: Longmans.Google Scholar
(1968) Linguistic analysis as a study of meaning. In F. R. Palmer (Ed.), Selected Papers of J. R. Firth, (1952–59) (pp. 12–26). London: Longmans.Google Scholar
Halliday, M.
(1966) Lexis as a linguistic level. In C. E. Bazell, J. C. Catford, M. A. K. Halliday, & R. H. Robins (Eds.), In memory of John Firth (pp. 148–162). London: Longman.Google Scholar
Hausmann, F.
(2007) Die Kollokationen im Rahmen der Phraseologie – Systematische und historische Darstellung. Zeitschrift für Anglistik und Amerikanistik, 55, 217–234. DOI logoGoogle Scholar
Jakubíček, M., Kilgarriff, A., Kovář, V., Rychlỳ, P., & Suchomel, V.
(2013) The tenten corpus family. In 7th international corpus linguistics conferencecl (p. 125–127).Google Scholar
Kilgarriff, A., Baisa, V., Bušta, J., Jakubíček, M., Kovář, V., Michelfeit, J., Suchomel, V.
(2014) The Sketch engine: ten years on. Lexicography, 1(1), 7–36. DOI logoGoogle Scholar
Kilgarriff, A., Rychlỳ, P., Smrz, P., & Tugwell, D.
(2004) The Sketch Engine. In G. Williams, & S. Vessier (Eds.), Proceedings of the 11th EURALEX International Congress (pp. 105–116). Lorient: Université de Bretagne-Sud.Google Scholar
Krebs, A., Lenci, A., & Paperno, D.
(2018) SemEval-2018 task 10: Capturing discriminative attributes. In Proceedings of the 12th international workshop on semantic evaluation (pp. 732–740). New Orleans, LA: Association for Computational Linguistics. DOI logoGoogle Scholar
Krebs, A., & Paperno, D.
(2016) Capturing discriminative attributes in a distributional space: Task proposal. In Proceedings of the 1st workshop on evaluating vector-space representations for NLP (pp. 51–54). DOI logoGoogle Scholar
Krenn, B., & Evert, S.
(2001) Can we do better than frequency? A case study on extracting PP-verb collocations. In Proceedings of the ACL Workshop on Collocations (pp. 39–46).Google Scholar
Lai, S., Leung, K. S., & Leung, Y.
(2018) SUNNYNLP at SemEval-2018 task 10: A support-vector-machine-based method for detecting semantic difference using taxonomy and word embedding features. In Proceedings of the 12th international workshop on semantic evaluation (pp. 741–746). New Orleans, LA: Association for Computational Linguistics. DOI logoGoogle Scholar
Lazaridou, A., Baroni, M.
, et al. (2016) The red one!: On learning to refer to things based on discriminative properties. In Proceedings of the 54th annual meeting of the association for computational linguistics (volume 2: Short papers) (pp. 213–218). DOI logoGoogle Scholar
Lee, L.
(1999) Measures of distributional similarity. In Proceedings of the 37th annual meeting of the association for computational linguistics on computational linguistics (pp. 25–32). Stroudsburg, PA: Association for Computational Linguistics. Retrieved from DOI logoGoogle Scholar
MacQueen, J.
(1967) Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, volume 1: Statistics (pp. 281–297). Berkeley, CA: University of California Press. Retrieved from [URL]Google Scholar
McRae, K., Cree, G. S., Seidenberg, M. S., & McNorgan, C.
(2005) Semantic feature production norms for a large set of living and nonliving things. Behavior research methods, 37(4), 547–559. DOI logoGoogle Scholar
Mcskimin, J. R.
(1977) The use of a semantic network in a deductive question- answering system. In Proc. IJCAI 5 (pp. 50–58).Google Scholar
Mihalcea, R., & Hassan, S.
(2017) Similarity. In R. Mitkov (Ed.), The Oxford Handbook of Computational Linguistics. Oxford: Oxford University Press.Google Scholar
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J.
(2013) Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (pp. 3111–3119).Google Scholar
Navigli, R., & Ponzetto, S. P.
(2012) Babelnet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial Intelligence, 193, 217–250. DOI logoGoogle Scholar
Oakes, M. P.
(1998) Statistics for Corpus Linguistics. Edinburgh University Press.Google Scholar
Pennington, J., Socher, R., & Manning, C.
(2014) Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532–1543). DOI logoGoogle Scholar
Santus, E., Biemann, C., & Chersoni, E.
(2018) BomJi at SemEval-2018 task 10: Combining vector-, pattern- and graph-based information to identify discriminative attributes. In Proceedings of the 12th international workshop on semantic evaluation (pp. 990–994). New Orleans, LA: Association for Computational Linguistics. DOI logoGoogle Scholar
Shiue, Y.-T., Huang, H.-H., & Chen, H.-H.
(2018) NTU NLP lab system at SemEval-2018 task 10: Verifying semantic differences by integrating dis- tributional information and expert knowledge. In Proceedings of the 12th international workshop on semantic evaluation (pp. 1027–1033). New Orleans, LA: Association for Computational Linguistics. DOI logoGoogle Scholar
Smadja, F. A., & McKeown, K. R.
(1990) Automatically extracting and representing collocations for language generation. In Proceedings of the 28th annual meeting of the association for computational linguistics (pp. 252–259). Pittsburgh, PA: Association for Computational Linguistics. Retrieved from [URL] DOI logoGoogle Scholar
(1990) Automatically extracting and representing collocations for language generation. In Proceedings of the 28th annual meeting of the association for computational linguistics (pp. 252–259). Pittsburgh, PA: Association for Computational Linguistics. Retrieved from [URL] DOI logoGoogle Scholar
Sowa, J. F.
(Ed.) 1991Principles of Semantic Networks. Explorations in the Representation of Knowledge. San Mateo, California: Morgan Kaufmann.Google Scholar
Speer, R., & Havasi, C.
(2013) Conceptnet 5: A large semantic network for relational knowledge. In The People’s Web meets NLP (pp. 161–176). Springer. DOI logoGoogle Scholar
Speer, R., & Lowry-Duda, J.
(2017) Conceptnet at semeval-2017 task 2: Extending word embeddings with multilingual relational knowledge. In Proceedings of the 11th international workshop on semantic evaluation (semeval- 2017) (pp. 85–89). DOI logoGoogle Scholar
(2018) Luminoso at SemEval-2018 task 10: Distinguishing attributes using text corpora and relational knowledge. In Proceedings of the 12th international workshop on semantic evaluation (pp. 985–989). New Orleans, LA: Association for Computational Linguistics. DOI logoGoogle Scholar
Stubbs, M.
(2002) Two quantitative methods of studying phraseology in English. International Journal of Corpus Linguistics, 7 (2), 215–244. DOI logoGoogle Scholar
Sussna, M.
(1993) Word sense disambiguation for free-text indexing using a massive semantic network. In Proceedings of the second international conference on information and knowledge management (pp. 67–74). New York, NY: ACM.Google Scholar
Taslimipoor, S., Rohanian, O., Ha, L. A., Corpas Pastor, G., & Mitkov, R.
(2018) Wolves at SemEval-2018 task 10: Semantic discrimination based on knowledge and association. In Proceedings of the 12th international workshop on semantic evaluation (pp. 972–976). New Orleans, LA: Association for Computational Linguistics. DOI logoGoogle Scholar
Trenkmann, M.
(2016) PhraseFinder – Search millions of books for language use. [URL] (Accessed: 2018–0130).Google Scholar
Turney, P. D., & Pantel, P.
(2010) From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research, 37(1), 141–188. Retrieved from [URL]. DOI logoGoogle Scholar
Vinayan, V., Anand Kumar, M., & Soman, K. P.
(2019) Capturing discriminative attributes using convolution neural network over conceptnet number-batch embedding. In V. Sridhar, M. Padma, & K. R. Rao (Eds.), Emerging research in electronics, computer science and technology (pp. 793–802). Singapore: Springer Singapore. DOI logoGoogle Scholar