| الوصف: |
Polysemy and Homonymy are two different kinds of lexical ambiguity. The main difference between them is that plysemous words can share the same alternation - where alternation is the senses a word can have - and homonymous words have idiosyncratic alternations. This means that, for instance, a word such as lamb, whose alternation is given by the senses food and animal, is a polysemous word, given that a number of other words share this very alternation food-animal, e.g. the word fish. On the other hand, a word such as ball, whose possible senses are of artifact and event, is homonymous, given that no other words share the alternation artifact-event. Furthermore, polysemy highlights two different aspects of the same lexical item, where homonymy describes the fact that the same lexical unit is used to represent two different and completely unrelated word-meanings. These two kinds of lexical ambiguity have even been an issue in lexicography, given that there is no clear rule used to distinguish between polysemous and homonymous words. As a matter of principle, we would expect to have different lexical entries for homonymous words, but only one lexical entry with internal differentiation for polysemous words. An important work needs to be mentioned here, that is the Generative Lexicon (Pustejovsky, 1995). This is a theoretical framework for lexical semantics which focuses on the compositionality of word meanings. In regard of polysemy and homonymy, GL provides a clear explanation of how it is possible to understand the appropriate sense of a word in a specific sentence. This is done by looking at the context in which the word appears, and, specifically, looking at the type of argument required by the predication. These phenomena have even been of interest among computational linguists, insomuch as they have tried to implement some models able to predict the alter- nations polysemous words can have. One of the most important work concerning this matter is the one made by Boleda, Pado, Utt (2012), in which a model is ... |
