Abstract
Word Sense Disambiguation (WSD) is the task of selecting the correct sense of a word in a context from a sense repository. Typically, WSD is approached as a supervised classification task to get state-of-the-art performance (e.g. [1]), and thus a large amount of sense-tagged examples for each sense of the word is needed, according to the word-expert approach. This requirement makes the supervised approach unfeasible for “all-words” tasks, consisting on disambiguating all the words in texts. This problem has been called the Knowledge Acquisition Bottleneck and many solutions have been proposed for it (see for example [2]) .
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Gliozzo, A.M., Ranieri, M., Strapparava, C. (2005). Crossing Parallel Corpora and Multilingual Lexical Databases for WSD. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2005. Lecture Notes in Computer Science, vol 3406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30586-6_26
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DOI: https://doi.org/10.1007/978-3-540-30586-6_26
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