A generic framework and methodology for extracting semantics from co-occurrences
|A generic framework and methodology for extracting semantics from co-occurrences|
|Author(s)||Rachakonda A.R., Srinivasa S., Kulkarni S., Srinivasan M.S.|
|Published in||Data and Knowledge Engineering|
|Keyword(s)||Co-occurrence, Cognitive models, Data mining, Text mining (Extra: Cognitive systems, Data mining, Co-occurrence, Co-occurrence Graph, Cognitive model, Cognitive science, Generic frameworks, Semantic associations, Text mining, Wikipedia articles, Semantics)|
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|Browse properties · List of literature reviews|
A generic framework and methodology for extracting semantics from co-occurrences is a 2014 literature review written in English by Rachakonda A.R., Srinivasa S., Kulkarni S., Srinivasan M.S. and published in Data and Knowledge Engineering.
Extracting 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.
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