Difference between revisions of "Towards Perfect Text Classification with Wikipedia-Based Semantic Naïve Bayes Learning"
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== Overview == | == Overview == | ||
Abstract This paper suggests a novel way of dramatically improving the Naive Bayes text classifier with semantic tensor space model for document representation. In work, authors intend to achieve a perfect text classification with the semantic Naive Bayes learning that incorporates the semantic concept [[features]] into term feature statistics; for this, the Naive Bayes learning is semantically augmented under the tensor space model where the ‘concept’ space is regarded as an independent space equated with the ‘term’ and ‘document’ spaces, and it is produced with concept-level informative [[Wikipedia]] pages associated with a given document corpus. Through extensive experiments using three popular document corpora including Reuters-21578, 20Newsgroups , and OHSUMED corpora, authors prove that the proposed method not only has superiority over the recent deep learning-based classification methods but also shows nearly perfect classification performance. | Abstract This paper suggests a novel way of dramatically improving the Naive Bayes text classifier with semantic tensor space model for document representation. In work, authors intend to achieve a perfect text classification with the semantic Naive Bayes learning that incorporates the semantic concept [[features]] into term feature statistics; for this, the Naive Bayes learning is semantically augmented under the tensor space model where the ‘concept’ space is regarded as an independent space equated with the ‘term’ and ‘document’ spaces, and it is produced with concept-level informative [[Wikipedia]] pages associated with a given document corpus. Through extensive experiments using three popular document corpora including Reuters-21578, 20Newsgroups , and OHSUMED corpora, authors prove that the proposed method not only has superiority over the recent deep learning-based classification methods but also shows nearly perfect classification performance. | ||
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+ | == Embed == | ||
+ | === Wikipedia Quality === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Kim, Han-Joon; Kim, Jiyun; Kim, Jinseog; Lim, Pureum. (2018). "[[Towards Perfect Text Classification with Wikipedia-Based Semantic Naïve Bayes Learning]]". Elsevier BV. DOI: 10.1016/j.neucom.2018.07.002. | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === English Wikipedia === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | {{cite journal |last1=Kim |first1=Han-Joon |last2=Kim |first2=Jiyun |last3=Kim |first3=Jinseog |last4=Lim |first4=Pureum |title=Towards Perfect Text Classification with Wikipedia-Based Semantic Naïve Bayes Learning |date=2018 |doi=10.1016/j.neucom.2018.07.002 |url=https://wikipediaquality.com/wiki/Towards_Perfect_Text_Classification_with_Wikipedia-Based_Semantic_Naïve_Bayes_Learning |journal=Elsevier BV}} | ||
+ | </nowiki> | ||
+ | </code> | ||
+ | |||
+ | === HTML === | ||
+ | <code> | ||
+ | <nowiki> | ||
+ | Kim, Han-Joon; Kim, Jiyun; Kim, Jinseog; Lim, Pureum. (2018). &quot;<a href="https://wikipediaquality.com/wiki/Towards_Perfect_Text_Classification_with_Wikipedia-Based_Semantic_Naïve_Bayes_Learning">Towards Perfect Text Classification with Wikipedia-Based Semantic Naïve Bayes Learning</a>&quot;. Elsevier BV. DOI: 10.1016/j.neucom.2018.07.002. | ||
+ | </nowiki> | ||
+ | </code> |
Revision as of 08:47, 19 November 2019
Authors | Han-Joon Kim Jiyun Kim Jinseog Kim Pureum Lim |
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Publication date | 2018 |
DOI | 10.1016/j.neucom.2018.07.002 |
Links | Original |
Towards Perfect Text Classification with Wikipedia-Based Semantic Naïve Bayes Learning - scientific work related to Wikipedia quality published in 2018, written by Han-Joon Kim, Jiyun Kim, Jinseog Kim and Pureum Lim.
Overview
Abstract This paper suggests a novel way of dramatically improving the Naive Bayes text classifier with semantic tensor space model for document representation. In work, authors intend to achieve a perfect text classification with the semantic Naive Bayes learning that incorporates the semantic concept features into term feature statistics; for this, the Naive Bayes learning is semantically augmented under the tensor space model where the ‘concept’ space is regarded as an independent space equated with the ‘term’ and ‘document’ spaces, and it is produced with concept-level informative Wikipedia pages associated with a given document corpus. Through extensive experiments using three popular document corpora including Reuters-21578, 20Newsgroups , and OHSUMED corpora, authors prove that the proposed method not only has superiority over the recent deep learning-based classification methods but also shows nearly perfect classification performance.
Embed
Wikipedia Quality
Kim, Han-Joon; Kim, Jiyun; Kim, Jinseog; Lim, Pureum. (2018). "[[Towards Perfect Text Classification with Wikipedia-Based Semantic Naïve Bayes Learning]]". Elsevier BV. DOI: 10.1016/j.neucom.2018.07.002.
English Wikipedia
{{cite journal |last1=Kim |first1=Han-Joon |last2=Kim |first2=Jiyun |last3=Kim |first3=Jinseog |last4=Lim |first4=Pureum |title=Towards Perfect Text Classification with Wikipedia-Based Semantic Naïve Bayes Learning |date=2018 |doi=10.1016/j.neucom.2018.07.002 |url=https://wikipediaquality.com/wiki/Towards_Perfect_Text_Classification_with_Wikipedia-Based_Semantic_Naïve_Bayes_Learning |journal=Elsevier BV}}
HTML
Kim, Han-Joon; Kim, Jiyun; Kim, Jinseog; Lim, Pureum. (2018). "<a href="https://wikipediaquality.com/wiki/Towards_Perfect_Text_Classification_with_Wikipedia-Based_Semantic_Naïve_Bayes_Learning">Towards Perfect Text Classification with Wikipedia-Based Semantic Naïve Bayes Learning</a>". Elsevier BV. DOI: 10.1016/j.neucom.2018.07.002.