Unsupervised Word Sense Disambiguation Using Alpha-Beta Associative Memories

Abstract

We present an alternative method to the use of overlapping as a distance measure in simple Lesk algorithm. This paper presents an algorithm that uses Alpha-Beta associative memory type Max and Min to measure a given ambiguous word’s meaning in relation to its context, assigning to the word the meaning that is most related. The principal advantage of using this algorithm is the ability to deal with inflectional and derivational forms of words, enabling the possibility of bypassing the stemming procedure of words involved in the disambiguation process. Different experiments were performed, with two parameters as variables: the context window size, and whether stemming was applied or not. The experimental results (F1-score) show that our algorithm performs better than the use of the overlapped metric in the simple Lesk algorithm. Moreover, the experiments show that as more information is added to the sense or meaning, and the overlap metric is used, the precision of the simple Lesk algorithm is decreased-in contrast to the performance of our algorithm.