Implicit readability ranking using the latent variable of a Bayesian Probit model

• 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.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Language Technology (hsv//eng)

Publication and Content Type

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