Document (#39340)

Author

Ko, Y.

Title

¬A new term-weighting scheme for text classification using the odds of positive and negative class probabilities

Source

Journal of the Association for Information Science and Technology. 66(2015) no.12, S.2553-2565

Year

2015

Abstract

Text classification (TC) is a core technique for text mining and information retrieval. It has been applied to many applications in many different research and industrial areas. Term-weighting schemes assign an appropriate weight to each term to obtain a high TC performance. Although term weighting is one of the important modules for TC and TC has different peculiarities from those in information retrieval, many term-weighting schemes used in information retrieval, such as term frequency-inverse document frequency (tf-idf), have been used in TC in the same manner. The peculiarity of TC that differs most from information retrieval is the existence of class information. This article proposes a new term-weighting scheme that uses class information using positive and negative class distributions. As a result, the proposed scheme, log tf-TRR, consistently performs better than do other schemes using class information as well as traditional schemes such as tf-idf.

Content

Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23338/abstract.

Theme

Automatisches Klassifizieren
Computerlinguistik

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5. Li, X.; Zhang, A.; Li, C.; Ouyang, J.; Cai, Y.: Exploring coherent topics by topic modeling with term weighting (2018) 0.33

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