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Open Computer Science

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Feature selection for spectral clustering: to help or not to help spectral clustering when performing sense discrimination for IR?

Adrian-Gabriel Chifu / Florentina Hristea
Published Online: 2018-12-31 | DOI: https://doi.org/10.1515/comp-2018-0021

Abstract

Whether or not word sense disambiguation (WSD) can improve information retrieval (IR) results represents a topic that has been intensely debated over the years, with many inconclusive or contradictory conclusions. The most rarely used type of WSD for this task is the unsupervised one, although it has been proven to be beneficial at a large scale. Our study builds on existing research and tries to improve the most recent unsupervised method which is based on spectral clustering. It investigates the possible benefits of “helping” spectral clustering through feature selection when it performs sense discrimination for IR. Results obtained so far, involving large data collections, encourage us to point out the importance of feature selection even in the case of this advanced, state of the art clustering technique that is known for performing its own feature weighting. By suggesting an improvement of what we consider the most promising approach to usage of WSD in IR, and by commenting on its possible extensions, we state that WSD still holds a promise for IR and hope to stimulate continuation of this line of research, perhaps at an even more successful level.

Keywords: word sense discrimination; information retrieval; query disambiguation; spectral clustering

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About the article

Received: 2018-10-21

Accepted: 2018-12-30

Published Online: 2018-12-31

Published in Print: 2018-12-01


Citation Information: Open Computer Science, Volume 8, Issue 1, Pages 218–227, ISSN (Online) 2299-1093, DOI: https://doi.org/10.1515/comp-2018-0021.

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© by Adrian-Gabriel Chifu, Florentina Hristea, published by De Gruyter. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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