Difference between revisions of "Document Topic Extraction based on Wikipedia Category"

From Wikipedia Quality
Jump to: navigation, search
(Basic information on Document Topic Extraction based on Wikipedia Category)
 
(+ links)
Line 1: Line 1:
'''Document Topic Extraction based on Wikipedia Category''' - scientific work related to Wikipedia quality published in 2011, written by Jiali Yun, Liping Jing, Jian Yu, Houkuan Huang and Ying Zhang.
+
'''Document Topic Extraction based on Wikipedia Category''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Jiali Yun]], [[Liping Jing]], [[Jian Yu]], [[Houkuan Huang]] and [[Ying Zhang]].
  
 
== Overview ==
 
== Overview ==
Document Topic Extraction aims at using several key phrases to describe the topics of documents. It can be applied in web document categorization and tagging, document clusters topic description and information retrieval tasks. In this paper, authors propose a Wikipedia category-based document topic extraction method. Document is mapped to a set of Wikipedia categories and is represented as graph structure in order to conserve the relationship between Wikipedia categories. Then, document topic can be extracted by clustering the related Wikipedia categories in the document collection. Experiment in real data shows Wikipedia category-based document topic extraction method achieves the better result than latent topic modeling method, such as LDA.
+
Document Topic Extraction aims at using several key phrases to describe the topics of documents. It can be applied in web document categorization and tagging, document clusters topic description and [[information retrieval]] tasks. In this paper, authors propose a [[Wikipedia]] category-based document topic extraction method. Document is mapped to a set of [[Wikipedia categories]] and is represented as graph structure in order to conserve the relationship between Wikipedia [[categories]]. Then, document topic can be extracted by clustering the related Wikipedia categories in the document collection. Experiment in real data shows Wikipedia category-based document topic extraction method achieves the better result than latent topic modeling method, such as LDA.

Revision as of 07:50, 23 July 2019

Document Topic Extraction based on Wikipedia Category - scientific work related to Wikipedia quality published in 2011, written by Jiali Yun, Liping Jing, Jian Yu, Houkuan Huang and Ying Zhang.

Overview

Document Topic Extraction aims at using several key phrases to describe the topics of documents. It can be applied in web document categorization and tagging, document clusters topic description and information retrieval tasks. In this paper, authors propose a Wikipedia category-based document topic extraction method. Document is mapped to a set of Wikipedia categories and is represented as graph structure in order to conserve the relationship between Wikipedia categories. Then, document topic can be extracted by clustering the related Wikipedia categories in the document collection. Experiment in real data shows Wikipedia category-based document topic extraction method achieves the better result than latent topic modeling method, such as LDA.