Difference between revisions of "Conceptual Hierarchical Clustering of Documents Using Wikipedia Knowledge"

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'''Conceptual Hierarchical Clustering of Documents Using Wikipedia Knowledge''' - scientific work related to Wikipedia quality published in 2011, written by Gerasimos Spanakis, Georgios Siolas and Andreas Stafylopatis.
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'''Conceptual Hierarchical Clustering of Documents Using Wikipedia Knowledge''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Gerasimos Spanakis]], [[Georgios Siolas]] and [[Andreas Stafylopatis]].
  
 
== Overview ==
 
== Overview ==
In this paper, authors propose a novel method for conceptual hierarchical clustering of documents using knowledge extracted from Wikipedia. A robust and compact document representation is built in real-time using the Wikipedia API. The clustering process is hierarchi- cal and creates cluster labels which are descriptive and important for the examined corpus. Experiments show that the proposed technique greatly improves over the baseline approach.
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In this paper, authors propose a novel method for conceptual hierarchical clustering of documents using knowledge extracted from [[Wikipedia]]. A robust and compact document representation is built in real-time using the Wikipedia API. The clustering process is hierarchi- cal and creates cluster labels which are descriptive and important for the examined corpus. Experiments show that the proposed technique greatly improves over the baseline approach.

Revision as of 08:35, 18 October 2019

Conceptual Hierarchical Clustering of Documents Using Wikipedia Knowledge - scientific work related to Wikipedia quality published in 2011, written by Gerasimos Spanakis, Georgios Siolas and Andreas Stafylopatis.

Overview

In this paper, authors propose a novel method for conceptual hierarchical clustering of documents using knowledge extracted from Wikipedia. A robust and compact document representation is built in real-time using the Wikipedia API. The clustering process is hierarchi- cal and creates cluster labels which are descriptive and important for the examined corpus. Experiments show that the proposed technique greatly improves over the baseline approach.