Abstract
Word sense disambiguation is the task to identify the intended meaning of an ambiguous word in a certain context, one of the central problems in natural language processing. This paper describes four novel supervised disambiguation methods which adapt some familiar algorithms. They built on the Vector Space Model using an automatically generated stop list and two different statistical methods of finding index terms. These proceedings allow a fully automated and language independent disambiguation. The first method is based upon Latent Semantic Analysis, an automatic indexing method employed for text retrieval. The second one disambiguates via co-occurrence vectors of the target word. Disambiguation relying on Naive Bayes uses the Naive Bayes Classifier and disambiguation relying on SenseClusters uses an unsupervised word sense discrimination technique. These methods were implemented and evaluated to experience their performance, to compare the different approaches and to draw conclusions about the main characteristic of supervised disambiguation. The results show that the classification approach using Naive Bayes is the most efficient, scalable and successful method.
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Schumacher, K. (2007). Four Methods for Supervised Word Sense Disambiguation. In: Kedad, Z., Lammari, N., Métais, E., Meziane, F., Rezgui, Y. (eds) Natural Language Processing and Information Systems. NLDB 2007. Lecture Notes in Computer Science, vol 4592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73351-5_28
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DOI: https://doi.org/10.1007/978-3-540-73351-5_28
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