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  Modelling novelty preference in word learning

Versteegh, M., Ten Bosch, L., & Boves, L. (2011). Modelling novelty preference in word learning. In Proceedings of the 12th Annual Conference of the International Speech Communication Association (Interspeech 2011), Florence, Italy (pp. 761-764).

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-000F-8286-A Version Permalink: http://hdl.handle.net/11858/00-001M-0000-000F-828A-2
Genre: Conference Paper

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Versteegh_Interspeech_2011.pdf (Publisher version), 359KB
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 Creators:
Versteegh, Maarten1, 2, Author              
Ten Bosch, Louis2, Author              
Boves, Lou2, Author
Affiliations:
1International Max Planck Research School for Language Sciences, MPI for Psycholinguistics, Max Planck Society, Nijmegen, NL, escidoc:1119545              
2Centre for Language Studies · Radboud University Nijmegen, escidoc:55238              

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 Abstract: This paper investigates the effects of novel words on a cognitively plausible computational model of word learning. The model is first familiarized with a set of words, achieving high recognition scores and subsequently offered novel words for training. We show that the model is able to recognize the novel words as different from the previously seen words, based on a measure of novelty that we introduce. We then propose a procedure analogous to novelty preference in infants. Results from simulations of word learning show that adding this procedure to our model speeds up training and helps the model attain higher recognition rates.

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Language(s): eng - English
 Dates: 20112011-08-28
 Publication Status: Published in print
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Title: Interspeech 2011: 12th Annual Conference of the International Speech Communication Association
Place of Event: Florence, Italy
Start-/End Date: 2011-08-28 - 2011-08-31

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Title: Proceedings of the 12th Annual Conference of the International Speech Communication Association (Interspeech 2011), Florence, Italy
Source Genre: Proceedings
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Pages: - Volume / Issue: 1 Sequence Number: - Start / End Page: 761 - 764 Identifier: -