论文信息 - Information Retrieval and Text Categorization with Semantic Indexing

Information Retrieval and Text Categorization with Semantic Indexing

In this paper, we present the effect of the semantic indexing using WordNet senses on the Information Retrieval (IR) and Text Categorization (TC) tasks. The documents have been sense-tagged using a Word Sense Disambiguation (WSD) system based on Specialized Hidden Markov Models (SHMMs). The preliminary results showed that a small improvement of the performance was obtained only in the TC task.

Paolo Rosso | Ferran Plà | Vicente Vidal | Antonio Molina | Daniel Jiménez

[1] O. Debande,et al. Information and Communication Technologies: A Tool Empowering and Developing the Horizon of the Learner. , 2004 .

[2] Paolo Rosso,et al. Semantic Text Categorization using the K Nearest Neighbours Method , 2003, IICAI.

[3] Julio Gonzalo,et al. Indexing with WordNet synsets can improve text retrieval , 1998, WordNet@ACL/COLING.

[4] Paolo Rosso,et al. The influence of semantics in IR using LSI and K-means clustering techniques , 2003, ISICT.

[5] Carlos F. Enguix,et al. A comparison of experiments with bisecting-spherical K-means clustering and SVD algorithms , 2002 .