Using WordNet to Disambiguate Word Senses for Text Classification

Abstract

Abstract. In this paper, we propose an automatic text classification method based on word sense disambiguation. We use "hood" algorithm to remove the word ambiguity so that each word is replaced by its sense in the context. The nearest ancestors of the senses of all the non-stopwords in a give document are selected as the classes for the given document. We apply our algorithm to Brown Corpus. The effectiveness is evaluated by comparing the classification results with the classification results using manual disambiguation offered by Princeton University.

Concept Forest: A New Ontology-assisted Text Document Similarity Measurement Method

“…The second approach focuses on word sense disambiguation [15,16,17,18] to address the synonymy and polysemy problems in natural language processing. However, this approach tries to determine an exact sense for a term, often resulting in misclassification of terms.…”

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

“…Liu, P. Scheuermann and X. Zhu [3] proposed a text classification method based on word sense disambiguation. This algorithm is applied to Brown Corpus.…”

“…Word sense is the meaning a word can take depending how it is used [10]. For instance, one of the synsets of 'bank' is {depository financial institution, bank, banking concern, banking company} and its gloss is (a financial institution that accepts deposits and channels the money into lending activities; "he cashed a check at the bank") [3].…”

An Approach to Word Sense Disambiguation Combining Modified Lesk and Bag-of-Words

“…"WordNet" is the most widely used online dictionary [5][6][7] maintaining "words and related meanings" as well as "relations among different words".…”