Supervised Word Sense Disambiguation using Python
نویسنده
چکیده
In this paper, we discuss the problem of Word Sense Disambiguation (WSD) and one approach to solving the lexical sample problem. We use training and test data from SENSEVAL-3 and implement methods based on Naı̈ve Bayes calculations, cosine comparison of word-frequency vectors, decision lists, and Latent Semantic Analysis. We also implement a simple classifier combination system that combines these classifiers into one WSD module. We then prove the effectiveness of our WSD module by participating in the Multilingual Chinese-English Lexical Sample Task from SemEval-2007.
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تاریخ انتشار 2007