Abstract
In this chapter, techniques for representing the multiple meanings of a single word are discussed. This is a growing area, and is particularly important in languages where polysemous and homonymous words are common. This includes English, but it is even more prevalent in Mandarin for example. The techniques discussed can broadly be classified as lexical word sense representation, and as word sense induction. The inductive techniques can be sub-classified as clustering -based or as prediction-based.
1a. In a literal, exact, or actual sense; not figuratively, allegorically, etc
1b. Used to indicate that the following word or phrase must be taken in its literal sense, usually to add emphasis
1c. colloq. Used to indicate that some (frequently conventional) metaphorical or hyperbolical expression is to be taken in the strongest admissible sense: ‘virtually, as good as’; (also) ‘completely, utterly, absolutely’ ...
2a With reference to a version of something, as a transcription, translation, etc.: in the very words, word for word
2b. In extended use. With exact fidelity of representation; faithfully
3a. With or by the letters (of a word). Obs. rare
3b. In or with regard to letters or literature. Obs. rare
The seven senses of literally, Oxford English
Dictionary, 3rd ed., 2011
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References
Agirre, Eneko and Aitor Soroa. 2007. Semeval-2007 task 02: Evaluating word sense induction and discrimination systems. In Proceedings of the 4th international workshop on semantic evaluations. SemEval ’07 , 7–12. Prague, Czech Republic: Association for Computational Linguistics.
Agirre, Eneko, David MartÃnez, Oier López De Lacalle, and Aitor Soroa. 2006. Evaluating and optimizing the parameters of an unsupervised graph-based WSD algorithm. In Proceedings of the first workshop on graph based methods for natural language processing, 89–96. Association for Computational Linguistics.
Bartunov, Sergey, Dmitry Kondrashkin, Anton Osokin, and Dmitry P. Vetrov. 2015. Breaking sticks and ambiguities with adaptive skip-gram. In CoRR. arXiv:1502.07257.
Basile, Pierpaolo, Annalina Caputo, and Giovanni Semeraro. 2014. An Enhanced lesk word sense disambiguation algorithm through a distributional semantic model. In Proceedings of COLING 2014, the 25th international conference on computational linguistics: Technical papers. Dublin, 1591–1600. Ireland: Dublin City University and Association for Computational Linguistics.
Bird, Steven, Ewan Klein, and Edward Loper. 2009. Natural language processing with Python. O’Reilly Media, Inc.
Chen, Xinxiong, Zhiyuan Liu, and Maosong Sun. 2014. A unified model for word sense representation and disambiguation. In EMNLP (Citeseer), 1025–1035.
De Smedt, Tom and Walter Daelemans. 2012. Pattern for python. The Journal of Machine Learning Research 13 (1): 2063–2067.
Deng, J., W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. 2009. ImageNet: A large-scale hierarchical image database. In CVPR09.
Finkelstein, Lev, Evgeniy Gabrilovich, Yossi Matias, Ehud Rivlin, Zach Solan, Gadi Wolfman, and Eytan Ruppin. 2001. Placing search in context: The concept revisited. In Proceedings of the 10th international conference on World Wide Web, 406–414. ACM.
Frey, Brendan J., and Delbert Dueck. 2007. Clustering by passing messages between data points. Science 315 (5814): 972–976.
Huang, Eric H., Richard Socher, Christopher D. Manning, and Andrew Y. Ng. 2012. Improving word representations via global context and multiple word proto-types. In Proceedings of the 50th annual meeting of the association for computational linguistics: Long papers, vol. 1, 873–882. Association for Computational Linguistics.
Iacobacci, Ignacio, Mohammad Taher Pilehvar, and Roberto Navigli. 2015. SensEmbed: Learning sense embeddings for word and relational similarity. In Proceedings of ACL, 95–105.
Kågebäck, Mikael, Fredrik Johansson, Richard Johansson, and Devdatt Dubhashi. 2015. Neural context embeddings for automatic discovery of word senses. In Proceedings of NAACL-HLT, 25–32.
Kilgarriff, Adam. 2004. How dominant is the commonest sense of a word? In Text, speech and dialogue: 7th international conference, TSD 2004, Brno, Czech Republic, September 8–11, 2004. Proceedings, eds. Petr Sojka, Ivan Kopecek, and Karel Pala, 103–111. Berlin, Heidelberg: Springer. ISBN: 978-3-540-30120-2. https://doi.org/10.1007/978-3-540-30120-2_14.
Kleinberg, Jon M. 2003. An impossibility theorem for clustering. In Advances in neural information processing systems, 463–470.
Levy, Omer and Yoav Goldberg. 2014. Neural word embedding as implicit matrix factorization. In Advances in neural information processing systems, 2177–2185.
Mihalcea, Rada, Timothy Anatolievich Chklovski, and Adam Kilgarriff. 2004. The senseval-3 english lexical sample task. In Association for computational linguistics.
Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv:1301.3781.
Miller, George A. 1995. WordNet: A lexical database for English. Communications of the ACM 38 (11): 39–41.
Moro, Andrea and Roberto Navigli. 2015. SemEval-2015 task 13: Multilingual all-words sense disambiguation and entity linking. In Proceedings of SemEval- 2015.
Moro, Andrea, Alessandro Raganato, and Roberto Navigli. 2014. Entity linking meets word sense disambiguation: A unified approach. Transactions of the Association for Computational Linguistics (TACL) 2: 231–244.
Navigli, Roberto and Simone Paolo Ponzetto. 2010. BabelNet: Building a very large multilingual semantic network. In Proceedings of the 48th annual meeting of the association for computational linguistics, 216–225. Association for Computational Linguistics.
Navigli, Roberto, Kenneth C. Litkowski, and Orin Hargraves. 2007. SemEval- 2007 task 07: Coarse-grained english all-words task. In Proceedings of the 4th international workshop on semantic evaluations. SemEval ’07, 30–35. Prague, Czech Republic: Association for Computational Linguistics.
Neelakantan, Arvind, Jeevan Shankar, Alexandre Passos, and Andrew McCallum. 2015. Efficient non-parametric estimation of multiple embeddings per word in vector space. arXiv:1504.06654.
Pantel, Patrick and Dekang Lin. 2002. Discovering word senses from text. In Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, 613–619. ACM.
Reisinger, Joseph and Raymond J. Mooney. 2010. Multi-prototype vector-space models of word meaning. In Human language technologies: The 2010 annual conference of the north american chapter of the association for computational linguistics, 109–117. Association for Computational Linguistics.
Schütze, Hinrich. 1998. Automatic word sense discrimination. Computational Linguistics. 24 (1): 97–123. ISSN: 0891-2017.
Schwenk, Holger. 2004. Efficient training of large neural networks for language modeling. In 2004 IEEE international joint conference on neural networks, 2004. Proceedings, 4, 3059–3064. IEEE.
Tengi, Randee I. 1998. Design and implementation of the WordNet lexical database and searching software. WordNet: An electronic lexical database, ed. Christiane (réd.) Fellbaum, 105. Cambridge: The MIT Press.
Tian, Fei, Hanjun Dai, Jiang Bian, Bin Gao, Rui Zhang, Enhong Chen, and Tie- Yan Liu. 2014. A probabilistic model for learning multi-prototype word embeddings. In COLING, 151–160.
White, Lyndon, Roberto Togneri, Wei Liu, and Mohammed Bennamoun. 2018. Finding word sense embeddings of known meaning. In 19th international conference on intelligent text processing and computational linguistics (CICLing).
Zipf, George Kingsley. 1945. The meaning-frequency relationship of words. The Journal of General Psychology 33 (2): 251–256.
Zipf, G.K. 1949. Human behavior and the principle of least effort: An introduction to human ecology. Cambridge: Addison-Wesley Press.
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White, L., Togneri, R., Liu, W., Bennamoun, M. (2019). Word Sense Representations. In: Neural Representations of Natural Language. Studies in Computational Intelligence, vol 783. Springer, Singapore. https://doi.org/10.1007/978-981-13-0062-2_4
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