Semi-supervised Word Sense Disambiguation Using the Web as Corpus

Abstract

Abstract. As any other classification task, Word Sense Disambiguation requires a large number of training examples. These examples, which are easily obtained for most of the tasks, are particularly difficult to obtain for this case. Based on this fact, in this paper we investigate the possibility of using a Webbased approach for determining the correct sense of an ambiguous word based only in its surrounding context. In particular, we propose a semi-supervised method that is specially suited to work with just a few training examples. The method considers the automatic extraction of unlabeled examples from the Web and their iterative integration into the training data set. The experimental results, obtained over a subset of ten nouns from the SemEval lexical sample task, are encouraging. They showed that it is possible to improve the baseline accuracy of classifiers such as Naïve Bayes and SVM using some unlabeled examples extracted from the Web.

Web query disambiguation using PageRank

“…Despite this scaling difficulty, however, the field of Web query disambiguation has been fruitfully exploited in recent years via several approaches, and a number of research works have dealt with the specific problem (Agirre, Ansa, Martinez, & Hovy, ; Cuadros & Rigau, ; Gong, Cheang, & Leong, , ; Guzman‐Cabera, Rosso, Montes‐y‐Gomez, Villasenor‐Pineda, & Pinto‐Avendano, ; Klapaftis & Manandhar, ; Moldovan & Mihalcea, ; Snasel, Moravec, & Pokorny, ; Stamou & Ntoulas, ; Stokoe, Oakes, & Tait, ; Varlamis & Stamou, ; Wang & Hoffmann, ).…”

Sense-based Information Retrieval System by using Jaccard Coefficient Based WSD Algorithm

“…Among them, this system is used WordNet within WSD for finding semantically related words [7,8]. Word sense disambiguation process is essential and useful for many applications.…”

A Novel Approach to Word Sense Disambiguation Based on Topical and Semantic Association

“…We can broadly overview two main approaches to WSD, namely, machine learning and external knowledge sources. The former further distinguishes between supervised learning [ 2 , 3 ] and unsupervised learning approach [ 4 , 5 ], whereas the latter further divides into knowledge-based [ 6 , 7 ] and corpus-based approaches [ 8 ]. These approaches based on the external resource usually have lower performance than the machine learning ways, but they have the advantage of a higher precision rate and a wider coverage.…”