Abstract
Distributional measures of semantic relatedness determine word similarity based on how frequently a pair of words appear in the same contexts. A typical method is to construct a word-context matrix, then re-weight it using some measure of association, and finally take the vector distance as a measure of similarity. This has largely been an unsupervised process, but in recent years more work has been done devising methods of using known sets of synonyms to enhance relatedness measures. This paper examines and expands on one such measure, which learns a weighting of a word-context matrix by measuring associations between words appearing in a given context and sets of known synonyms. In doing so we propose a general method of learning weights for word-context matrices, and evaluate it on a word similarity task. This method works with a variety of measures of association and can be trained with synonyms from any resource.
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References
Turney, P.D., Pantel, P.: From frequency to meaning: Vector space models of semantics. Journal of Artificial Intelligence Research 37, 141–188 (2010)
Patwardhan, S.: Incorporating dictionary and corpus information into a vector measure of semantic relatedness. Master’s thesis, University of Minnesota, Duluth (2003)
Weeds, J., Weir, D.: Co-occurrence retrieval: A flexible framework for lexical distributional similarity. Comput. Linguist. 31, 439–475 (2005)
Mohammad, S., Hirst, G.: Distributional measures of concept-distance: A task-oriented evaluation. In: Jurafsky, D., Gaussier, É. (eds.) EMNLP, pp. 35–43. ACL (2006)
Hagiwara, M., Ogawa, Y., Toyama, K.: Supervised synonym acquisition using distributional features and syntactic patterns. Journal of Natural Language Processing 16, 59–83 (2005)
Kennedy, A., Szpakowicz, S.: A Supervised Method of Feature Weighting for Measuring Semantic Relatedness. In: Butz, C., Lingras, P. (eds.) Canadian AI 2011. LNCS, vol. 6657, pp. 222–233. Springer, Heidelberg (2011)
Yih, W.T.: Learning term-weighting functions for similarity measures. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, EMNLP 2009, vol. 2, pp. 793–802. Association for Computational Linguistics, Morristown (2009)
Hajishirzi, H., Yih, W.T., Kolcz, A.: Adaptive near-duplicate detection via similarity learning. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 419–426. ACM, New York (2010)
Broda, B., Piasecki, M.: Supermatrix: a general took for lexical semantic knowledge acquisition. Technical report, Institute of Applied Informatics, Wroclaw University of Technology, Poland (2008)
Evert, S.: The statistics of word cooccurrences: word pairs and collocations. Ph.D. thesis, Institut für maschinelle Sprachverarbeitung, Universität Stuttgart (2004)
Lin, D.: Automatic retrieval and clustering of similar words. In: Proceedings of the 17th International Conference on Computational Linguistics, pp. 768–774. Association for Computational Linguistics, Morristown (1998)
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Kennedy, A., Szpakowicz, S. (2012). Supervised Distributional Semantic Relatedness. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2012. Lecture Notes in Computer Science(), vol 7499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32790-2_25
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DOI: https://doi.org/10.1007/978-3-642-32790-2_25
Publisher Name: Springer, Berlin, Heidelberg
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