Abstract
We present an integer linear programming model of word sense disambiguation. Given a sentence, an inventory of possible senses per word, and a sense relatedness measure, the model assigns to the sentence’s word occurrences the senses that maximize the total pairwise sense relatedness. Experimental results show that our model, with two unsupervised sense relatedness measures, compares well against two other prominent unsupervised word sense disambiguation methods.
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Panagiotopoulou, V., Varlamis, I., Androutsopoulos, I., Tsatsaronis, G. (2012). Word Sense Disambiguation as an Integer Linear Programming Problem. In: Maglogiannis, I., Plagianakos, V., Vlahavas, I. (eds) Artificial Intelligence: Theories and Applications. SETN 2012. Lecture Notes in Computer Science(), vol 7297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30448-4_5
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DOI: https://doi.org/10.1007/978-3-642-30448-4_5
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