DocumentCode :
3039238
Title :
Topic Modelling Used to Improve Arabic Web Pages Clustering
Author :
Alghamdi, Hanan ; Selamat, Ali
Author_Institution :
Fac. of Comput., Univ. Teknol. Malaysia, Johor Bahru, Malaysia
fYear :
2015
fDate :
26-29 April 2015
Firstpage :
1
Lastpage :
6
Abstract :
Topic modelling main purpose is to have machine-understandable and semantic annotation to textual contents of Web.It aim to extract knowledge rather than unrelated information. In this paper, we evaluate the impact of using topic model (which intended to represent the documents like a combination of topics where each topic is a mix of vectors) in improving documents clustering results. We have compared the results of clustering using PLSA or LSA. The experiments performed on a set of common newspaper websites that have highly dimensional data and we use Purity, Mean intra-cluster distance (MICD) and Davies-Bouldin index (DBI) for clustering evaluation. Thus, we acquired favorable clustering results, especially in the context of the Arabic language as PLSA were effective in minimizing MICD, expanding purity and bringing down DBI.
Keywords :
Internet; knowledge acquisition; natural language processing; pattern clustering; probability; text analysis; Arabic Web page clustering; DBI; Davies-Bouldin index; LDA; MICD; PLSA; Web textual content; knowledge extraction; latent Dirichlet allocation; mean intracluster distance; probabilistic latent semantic analysis; semantic annotation; topic modelling; Clustering algorithms; Computational modeling; Indexes; Matrix decomposition; Probabilistic logic; Semantics; Web pages;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cloud Computing (ICCC), 2015 International Conference on
Conference_Location :
Riyadh
Print_ISBN :
978-1-4673-6617-5
Type :
conf
DOI :
10.1109/CLOUDCOMP.2015.7149662
Filename :
7149662
Link To Document :
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