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A parallel approach to context-based term weighting

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2 Author(s)
Arora, S. ; Dept. of Inf. Technol., Netaji Subhas Inst. of Technol., New Delhi, India ; Chakravarty, S.

Information retrieval and extraction essentially rely on estimating the relevance of words present in a large corpus of documents or text. One of the approaches to measuring relevance is analyzing the importance of words based on their statistical distribution within a document. Quite another approach ensues from their linguistic relevance within a logically perceived context. Literature presents a body of work done employing both statistical as well as contextual approaches. The challenge currently is on enhancing the performance of document analysis and clustering systems. Ever since we witnessed a massive explosion of information and raw data available on the web, their analysis demands more rigorous computations and processing. Given the widely distributed environment as a backbone platform for these systems to operate, there is an urgent need to develop techniques to scale up their performance on multiple processors. We propose a parallelized strategy to estimate the statistical as well as contextual relevance of words, employing master-slave configuration on a cluster of processors. Our parallel algorithm has been successfully tested on a self-made Beowulf cluster comprising ten nodes, showing significant performance improvement over single processor.

Published in:

Information and Communication Technologies (WICT), 2011 World Congress on

Date of Conference:

11-14 Dec. 2011

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