2009 | OriginalPaper | Buchkapitel
New Semantic Similarity Based Model for Text Clustering Using Extended Gloss Overlaps
verfasst von : Walaa K. Gad, Mohamed S. Kamel
Erschienen in: Machine Learning and Data Mining in Pattern Recognition
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Most text clustering techniques are based on words and/or phrases weights in the text. Such representation is often unsatisfactory because it ignores the relationships between terms, and considers them as independent features.
In this paper, a new semantic similarity based model (SSBM) is proposed. The semantic similarity based model computes semantic similarities by utilizing WordNet as an ontology. The proposed model captures the semantic similarities between documents that contain semantically similar terms but unnecessarily syntactically identical.
The semantic similarity based model assigns a new weight to document terms reflecting the semantic relationships between terms that co-occur literally in the document. Our model in conjunction with the extended gloss overlaps measure and the adapted Lesk algorithm solves ambiguity, synonymy problems that are not detected using traditional term frequency based text mining techniques.
The proposed model is evaluated on the Reuters-21578 and the 20-Newsgroups text collections datasets. The performance is assessed in terms of the Fmeasure, Purity and Entropy quality measures. The obtained results show promising performance improvements compared to the traditional term based vector space model (VSM) as well as other existing methods that include semantic similarity measures in text clustering.