The efficiency of corpus-based distributional models for literature-based discovery on large data sets

Symonds, Michael, Bruza, Peter D., & Sitbon, Laurianne (2014) The efficiency of corpus-based distributional models for literature-based discovery on large data sets. In Proceedings of the Second Australasian Web Conference [Conferences in Research and Practice in Information Technology, Volume 155], Australian Computer Society Inc., Auckland, pp. 49-57.

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Abstract

This paper evaluates the efficiency of a number of popular corpus-based distributional models in performing discovery on very large document sets, including online collections. Literature-based discovery is the process of identifying previously unknown connections from text, often published literature, that could lead to the development of new techniques or technologies.

Literature-based discovery has attracted growing research interest ever since Swanson's serendipitous discovery of the therapeutic effects of fish oil on Raynaud's disease in 1986.

The successful application of distributional models in automating the identification of indirect associations underpinning literature-based discovery has been heavily demonstrated in the medical domain. However, we wish to investigate the computational complexity of distributional models for literature-based discovery on much larger document collections, as they may provide computationally tractable solutions to tasks including, predicting future disruptive innovations.

In this paper we perform a computational complexity analysis on four successful corpus-based distributional models to evaluate their fit for such tasks. Our results indicate that corpus-based distributional models that store their representations in fixed dimensions provide superior efficiency on literature-based discovery tasks.

Impact and interest:

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ID Code: 63726
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: Distributional Models, discovery, literature-based discovery, paradigmatic associations, corpus-based
ISBN: 978-1-921770-37-1
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Organisation of Information and Knowledge Resources (080707)
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 [please consult the author]
Deposited On: 27 Oct 2013 23:31
Last Modified: 09 May 2014 00:44

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