Open Directory Project (ODP) has been successfully utilized in text classification due to its representation ability of various categories. However, ODP includes a limited number of entities, which play an important role in classification tasks. In this paper, we enrich the semantics of ODP categories with Probase entities. To effectively incorporate Probase entities in ODP categories, we first represent each ODP category and Probase entity in terms of concepts. Next, we measure the semantic relevance between an ODP category and a Probase entity based on the concept vector. Finally, we use Probase entity to enrich the semantics of the ODP categories. Our experimental results show that the proposed methodology exhibits a significant improvement over state-of-the-art techniques in the ODP-based text classification.
In another line of work, several studies @cite_12 @cite_21 have enriched the semantics of categories in classifier using knowledge base, such as Wikipedia or WordNet. In particular, @cite_12 has employed Wikipedia to enrich the semantics of ODP categories with phrases and hyperlinks for the ODP-based text classification. However, the coverage of additional semantic information is limited because the average number of Wikipedia phrases per ODP document is approximately 0.5 @cite_12 . In addition, it might contain Wikipedia phrases, which have little relevance to the ODP category, such as Wikipedia phrase service plan" in the ODP category .
@cite_21 @cite_12
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Automatic Text Categorization (TC) is a complex and useful task for many natural language applications, and is usually performed through the use of a set of manually classified documents, a training collection. We suggest the utilization of additional resources like lexical databases to increase the amount of information that TC systems make use of, and thus, to improve their performance. Our approach integrates WordNet information with two training approaches through the Vector Space Model. The training approaches we test are the Rocchio (relevance feedback) and the Widrow-Hoff (machine learning) algorithms. Results obtained from evaluation show that the integration of WordNet clearly outperforms training approaches, and that an integrated technique can effectively address the classification of low frequency categories. Traditional Open Directory Project (ODP)-based text classification methods use bag-of-words approach, which only utilizes single words in ODP documents and ignores important types of semantic information such as phrases and related terms. In this paper, we propose a method for enriching the semantic information in ODP documents by utilizing Wikipedia knowledge. First, we construct a phrase dictionary based on Wikipedia and search for Wikipedia phrases in ODP documents. Second, we select the most likely relevant Wikipedia articles and relevant hyperlinks for Wikipedia phrases in ODP documents. Finally, we add Wikipedia phrases and relevant hyperlinks to ODP documents to enrich the semantic information. Our evaluation results verify the efficacy of the proposed methodology.