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Path-based methods on categorical structures for conceptual representation of wikipedia articles

Abstrakt

Machine learning algorithms applied to text categorization mostly employ the Bag of Words (BoW) representation to describe the content of the documents. This method has been successfully used in many applications, but it is known to have several limitations. One way of improving text representation is usage of Wikipedia as the lexical knowledge base – an approach that has already shown promising results in many research studies. In this paper we propose three path-based measures for computing document relatedness in the conceptual space formed by the hierarchical organization of a Wikipedia Category Graph (WCG). We compare the proposed approaches with the standard Path Length method to establish the best relatedness measure for the WCG representation. To test overall WCG efficiency, we compare the proposed representations with the BoW method. The evaluation was performed with two different types of clustering algorithms (OPTICS and K-Means), used for categorization of keyword-based search results. The experiments have shown that our approach outperforms the standard Path Length approach, and the WCG representation achieves better results than BoW.

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Kategoria:
Publikacja w czasopiśmie
Typ:
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS nr 48, wydanie 2, strony 309 - 327,
ISSN: 0925-9902
Język:
angielski
Rok wydania:
2017
Opis bibliograficzny:
Kucharczyk Ł., Szymański J.: Path-based methods on categorical structures for conceptual representation of wikipedia articles// JOURNAL OF INTELLIGENT INFORMATION SYSTEMS. -Vol. 48, iss. 2 (2017), s.309-327
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.1007/s10844-016-0416-5
Weryfikacja:
Politechnika Gdańska

wyświetlono 113 razy

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