Word Sense Disambiguation using Static and Dynamic Sense Vectors

It is popular in WSD to use contextual information in training sense tagged data. Co-occurring words within a limited window-sized context support one sense among the semantically ambiguous ones of the word. This paper reports on word sense disambiguation of English words using static and dynamic sense vectors. First, context vectors are constructed using contextual words 1 in the training sense tagged data. Then, the words in the context vector are weighted with local density. Using the whole training sense tagged data, each sense of a target word2 is represented as a static sense vector in word space, which is the centroid of the context vectors. Then contextual noise is removed using a automatic selective sampling. A automatic selective sampling method use information retrieval technique, so as to enhance the discriminative power. In each test case, a automatic selective sampling method retrieves N relevant training samples to reduce noise. Using them, we construct another sense vectors for each sense of the target word. They are called dynamic sense vectors because they are changed according to a target word and its context. Finally, a word sense of a target word is determined using static and dynamic sense vectors. The English SENSEVAL test suit is used for this experimentation and our method produces relatively good results. 1 ‘Contextual words’ is defined as a list of content words in context. 2 In this paper, a target word ‘Wt’ is a semantically