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Content Based Mining and Extraction from Documents Using Topic Modelling


Author(s):

Dr.C.Sundar , Christian College of Engineering and Technology, oddanchatram; Indhumathi.Y, Christian College of Engineering and Technology, oddanchatram; Jeyapprathaa.S, Christian College of Engineering and Technology, oddanchatram

Keywords:

Topic Model, Information Filtering,Pattern Mining, Relevance Ranking,User Interest Model

Abstract:

Many mature term-based or pattern-based approaches have been used in the field of information filtering to generate users’ information needs from a collection of documents. A fundamental assumption for these approaches is that the documents in the collection are all about one topic. However, in reality users’ interests can be diverse and the documents in the collection often involve multiple topics. Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, and this has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering has not been so well explored. Patterns are always thought to be more discriminative than single terms for describing documents. However, the enormous amount of discovered patterns hinder them from being effectively and efficiently used in real applications, therefore, selection of the most discriminative and representative patterns from the huge amount of discovered patterns becomes crucial. To deal with the above mentioned limitations and problems, in this paper, a novel information filtering model, Polysemy based semantic approach is proposed.


Other Details:

Manuscript Id :IJSTEV2I10045
Published in :Volume : 2, Issue : 10
Publication Date: 01/05/2016
Page(s): 161-164
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