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Implicit readability ranking using the latent variable of a Bayesian Probit model
SICS East Swedish ICT AB. (NLPLAB)ORCID iD: 0000-0002-6357-4461
SICS East Swedish ICT AB.ORCID iD: 0000-0003-4899-588X
2016 (English)In: CL4LC 2016 - Computational Linguistics for Linguistic Complexity: Proceedings of the Workshop, 2016, 104-112 p.Conference paper, Published paper (Refereed)
Abstract [en]

Data driven approaches to readability analysis for languages other than English has been plagued by a scarcity of suitable corpora.  Often, relevant corpora consist only of easy-to-read texts with no  rank  information  or  empirical  readability  scores,  making  only  binary  approaches,  such  as classification, applicable.  We propose a Bayesian, latent variable, approach to get the most out of these kinds of corpora. In this paper we present results on using such a model for readability ranking. The model is evaluated on a preliminary corpus of ranked student texts with encourag- ing results.  We also assess the model by showing that it performs readability classification on par with a state of the art classifier while at the same being transparent enough to allow more sophisticated interpretations.

Place, publisher, year, edition, pages
2016. 104-112 p.
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:liu:diva-133783ISBN: 9784879747099 (electronic)OAI: oai:DiVA.org:liu-133783DiVA: diva2:1063079
Conference
Coling 2016 Workshop on Computational Linguistics for Linguistic Complexity (CL4LC), Osaka, Japan, 11 December 2016
Available from: 2017-01-09 Created: 2017-01-09 Last updated: 2017-01-20Bibliographically approved

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Falkenjack, JohanJönsson, Arne
Language Technology (Computational Linguistics)

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • Other style
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Language
  • de-DE
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  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
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  • asciidoc
  • rtf