A Comparative Study for WordNet Guided Text Representation
نویسندگان
چکیده
Text information processing depends critically on the proper text representation. A common and naïve way of representing a document is a bag of its component words [1], but the semantic relations between words are ignored, such as synonymy and hypernymy-hyponymy between nouns. This paper presents a model for representing a document in terms of the synonymy sets (synsets) in WordNet [2]. The synsets stand for concepts corresponding to the words of the document. The Vector Space Model describes a document as orthogonal term vectors. We replace terms with concepts to build Concept Vector Space Model (CVSM) for the training set. Our experiments on the Reuters Corpus Volume I (RCV1) dataset have shown that the result is satisfactory.
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