Chinese Text Filtering based on Domain Keywords Extracted from Wikipedia

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Chinese Text Filtering based on Domain Keywords Extracted from Wikipedia
Authors
Xiang Wang
Hu Li
Yan Jia
SongChang Jin
Publication date
2013
DOI
10.1007/978-3-642-34522-7_104
Links
Original

Chinese Text Filtering based on Domain Keywords Extracted from Wikipedia - scientific work related to Wikipedia quality published in 2013, written by Xiang Wang, Hu Li, Yan Jia and SongChang Jin.

Overview

Several machine learning and information retrieval algorithms have been used for text filtering. All these methods have a common ground that they need positive and negative examples to build user profile. However, not all applications can get good training documents. In this paper, authors present a Wikipedia based method to build user profile without any other training documents. The proposed method extracts keywords of a special category from Wikipedia taxonomy and computes the weights of the extracted keywords based on Wikipedia pages. Experiment results on Chinese news text dataset SogouC show that the proposed method achieves good performance.

Embed

Wikipedia Quality

Wang, Xiang; Li, Hu; Jia, Yan; Jin, SongChang. (2013). "[[Chinese Text Filtering based on Domain Keywords Extracted from Wikipedia]]". Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-34522-7_104.

English Wikipedia

{{cite journal |last1=Wang |first1=Xiang |last2=Li |first2=Hu |last3=Jia |first3=Yan |last4=Jin |first4=SongChang |title=Chinese Text Filtering based on Domain Keywords Extracted from Wikipedia |date=2013 |doi=10.1007/978-3-642-34522-7_104 |url=https://wikipediaquality.com/wiki/Chinese_Text_Filtering_based_on_Domain_Keywords_Extracted_from_Wikipedia |journal=Springer, Berlin, Heidelberg}}

HTML

Wang, Xiang; Li, Hu; Jia, Yan; Jin, SongChang. (2013). &quot;<a href="https://wikipediaquality.com/wiki/Chinese_Text_Filtering_based_on_Domain_Keywords_Extracted_from_Wikipedia">Chinese Text Filtering based on Domain Keywords Extracted from Wikipedia</a>&quot;. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-34522-7_104.