NDLI: Arabic texts analysis for topic modeling evaluation

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Arabic texts analysis for topic modeling evaluation

Content Provider	Springer Nature Link
Author	Brahmi, Abderrezak Ech Cherif, Ahmed Benyettou, Abdelkader
Copyright Year	2011
Abstract	Significant progress has been made in information retrieval covering text semantic indexing and multilingual analysis. However, developments in Arabic information retrieval did not follow the extraordinary growth of Arabic usage in the Web during the ten last years. In the tasks relating to semantic analysis, it is preferable to directly deal with texts in their original language. Studies on topic models, which provide a good way to automatically deal with semantic embedded in texts, are not complete enough to assess the effectiveness of the approach on Arabic texts. This paper investigates several text stemming methods for Arabic topic modeling. A new lemma-based stemmer is described and applied to newspaper articles. The Latent Dirichlet Allocation model is used to extract latent topics from three Arabic real-world corpora. For supervised classification in the topics space, experiments show an improvement when comparing to classification in the full words space or with root-based stemming approach. In addition, topic modeling with lemma-based stemming allows us to discover interesting subjects in the press articles published during the 2007–2009 period.
Starting Page	33
Ending Page	53
Page Count	21
File Format	PDF
ISSN	13864564
Journal	Information Retrieval
Volume Number	15
Issue Number	1
e-ISSN	15737659
Language	English
Publisher	Springer Netherlands
Publisher Date	2011-06-23
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Arabic stemming Topic model Linguistic analysis Classification Test collections Data Structures, Cryptology and Information Theory Data Mining and Knowledge Discovery Document Preparation and Text Processing Pattern Recognition Information Storage and Retrieval
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems

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