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Reflective Random Indexing and indirect inference: a scalable method for discovery of implicit connections.

Abstract：

:The discovery of implicit connections between terms that do not occur together in any scientific document underlies the model of literature-based knowledge discovery first proposed by Swanson. Corpus-derived statistical models of semantic distance such as Latent Semantic Analysis (LSA) have been evaluated previously as methods for the discovery of such implicit connections. However, LSA in particular is dependent on a computationally demanding method of dimension reduction as a means to obtain meaningful indirect inference, limiting its ability to scale to large text corpora. In this paper, we evaluate the ability of Random Indexing (RI), a scalable distributional model of word associations, to draw meaningful implicit relationships between terms in general and biomedical language. Proponents of this method have achieved comparable performance to LSA on several cognitive tasks while using a simpler and less computationally demanding method of dimension reduction than LSA employs. In this paper, we demonstrate that the original implementation of RI is ineffective at inferring meaningful indirect connections, and evaluate Reflective Random Indexing (RRI), an iterative variant of the method that is better able to perform indirect inference. RRI is shown to lead to more clearly related indirect connections and to outperform existing RI implementations in the prediction of future direct co-occurrence in the MEDLINE corpus.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Cohen T,Schvaneveldt R,Widdows D

doi

10.1016/j.jbi.2009.09.003

subject

Has Abstract

pub_date

2010-04-01 00:00:00

pages

240-56

issue

2

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(09)00120-8

journal_volume

43

pub_type

杂志文章

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