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A generic framework and methodology for extracting semantics from co-occurrences
Abstract Extracting semantic associations from textExtracting semantic associations from text corpora is an important problem with several applications. It is well understood that semantic associations from text can be discerned by observing patterns of co-occurrences of terms. However, much of the work in this direction has been piecemeal, addressing specific kinds of semantic associations. In this work, we propose a generic framework, using which several kinds of semantic associations can be mined. The framework comprises a co-occurrence graph of terms, along with a set of graph operators. A methodology for using this framework is also proposed, where the properties of a given semantic association can be hypothesized and tested over the framework. To show the generic nature of the proposed model, four different semantic associations are mined over a corpus comprising of Wikipedia articles. The design of the proposed framework is inspired from cognitive science - specifically the interplay between semantic and episodic memory in humans. © 2014 Elsevier B.V. All rights reserved. © 2014 Elsevier B.V. All rights reserved.
Abstractsub Extracting semantic associations from textExtracting semantic associations from text corpora is an important problem with several applications. It is well understood that semantic associations from text can be discerned by observing patterns of co-occurrences of terms. However, much of the work in this direction has been piecemeal, addressing specific kinds of semantic associations. In this work, we propose a generic framework, using which several kinds of semantic associations can be mined. The framework comprises a co-occurrence graph of terms, along with a set of graph operators. A methodology for using this framework is also proposed, where the properties of a given semantic association can be hypothesized and tested over the framework. To show the generic nature of the proposed model, four different semantic associations are mined over a corpus comprising of Wikipedia articles. The design of the proposed framework is inspired from cognitive science - specifically the interplay between semantic and episodic memory in humans. © 2014 Elsevier B.V. All rights reserved. © 2014 Elsevier B.V. All rights reserved.
Bibtextype article  +
Doi 10.1016/j.datak.2014.06.002  +
Has author Rachakonda A.R. + , Srinivasa S. + , Sayali Kulkarni + , Srinivasan M.S. +
Has extra keyword Cognitive systems + , Data mining + , Co-occurrence + , Co-occurrence Graph + , Cognitive model + , Cognitive science + , Generic frameworks + , Semantic associations + , Text mining + , Wikipedia articles + , Semantics +
Has keyword Co-occurrence + , Cognitive models + , Data mining + , Text mining +
Issn 0169023X  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 39–59  +
Published in Data and Knowledge Engineering +
Title A generic framework and methodology for extracting semantics from co-occurrences +
Type literature review  +
Volume 92  +
Year 2014 +
Creation dateThis property is a special property in this wiki. 6 November 2014 11:17:05  +
Categories Publications without license parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Literature reviews  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 11:17:05  +
DateThis property is a special property in this wiki. 2014  +
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A generic framework and methodology for extracting semantics from co-occurrences + Title
 

 

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