David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Topics in Cognitive Science 3 (1):92-122 (2011)
The focus of this paper is two-fold. First, similarities generated from six semantic models were compared to human ratings of paragraph similarity on two datasets—23 World Entertainment News Network paragraphs and 50 ABC newswire paragraphs. Contrary to findings on smaller textual units such as word associations (Griffiths, Tenenbaum, & Steyvers, 2007), our results suggest that when single paragraphs are compared, simple nonreductive models (word overlap and vector space) can provide better similarity estimates than more complex models (LSA, Topic Model, SpNMF, and CSM). Second, various methods of corpus creation were explored to facilitate the semantic models’ similarity estimates. Removing numeric and single characters, and also truncating document length improved performance. Automated construction of smaller Wikipedia-based corpora proved to be very effective, even improving upon the performance of corpora that had been chosen for the domain. Model performance was further improved by augmenting corpora with dataset paragraphs
|Keywords||Wikipedia corpora Corpus preprocessing Corpus construction Semantic models Paragraph similarity|
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References found in this work BETA
Michael D. Lee & Elissa Y. Corlett (2003). Sequential Sampling Models of Human Text Classification. Cognitive Science 27 (2):159-193.
Citations of this work BETA
Thomas M. Gruenenfelder, Gabriel Recchia, Tim Rubin & Michael N. Jones (2015). Graph‐Theoretic Properties of Networks Based on Word Association Norms: Implications for Models of Lexical Semantic Memory. Cognitive Science 40 (1):n/a-n/a.
Danielle S. McNamara (2011). Computational Methods to Extract Meaning From Text and Advance Theories of Human Cognition. Topics in Cognitive Science 3 (1):3-17.
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