A Broad-Coverage Word Sense Tagger
نویسنده
چکیده
In other words, previous corpus-based WSD algorithms learn to disambiguate a polysemous word from previous usages of the same word. This has several undesirable consequences. Firstly, a word must occur thousands of times before a good classifter can be trained. There are thousands of polysemous words, e.g., 11,562 polysemous nouns in WordNet (Miller, 1990). For every polysemous word to occur thousands of times each, the corpus must contain billions of words. Secondly, learning to disambiguate a word from the previous usages of the same word means that whatever was learned for one word is not used on other words, which obviously missed generality in natural languages. Thirdly, these algorithms cannot deal with words for which classifiers have not been trained, which explains why most previous WSD algorithms only deal with a dozen of polysemous words. We demonstrate a new WSD algorithm that relies on a different intuition:
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