Traditional clustering algorithms do not consider the semantic relationships among words so that cannot accurately represent the meaning of documents. To overcome this problem, introducing semantic information from ontology such as WordNet has been widely used to improve the quality of text clustering. However, there still exist several challenges, such as synonym and polysemy, high dimensionality, extracting core semantics from texts, and assigning appropriate description for the generated clusters. In this paper, we report our attempt towards integrating WordNet with lexical chains to alleviate these problems. The proposed approach exploits ontology hierarchical structure and relations to provide a more accurate assessment of the similarity between terms for word sense disambiguation. Furthermore, we introduce lexical chains to extract a set of semantically related words from texts, which can represent the semantic content of the texts. Although lexical chains have been extensively used in text summarization, their potential impact on text clustering problem has not been fully investigated. Our integrated way can identify the theme of documents based on the disambiguated core features extracted, and in parallel downsize the dimensions of feature space. The experimental results using the proposed framework on reuters-21578 show that clustering performance improves significantly compared to several classical methods.
- Word Sense Disambiguation and Lexical Chains Construction Using Wordnet (2010)