Abbreviated text input using language modeling.

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Abbreviated text input using language modeling.

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dc.contributor.author Shieber, Stuart
dc.contributor.author Nelken, Rani
dc.date.accessioned 2008-08-14T16:29:22Z
dc.date.issued 2007
dc.identifier.citation Stuart M. Shieber and Rani Nelken. Abbreviated text input using language modeling. Natural Language Engineering, 13(2):165-183, June 2007. en
dc.identifier.issn 1351-3249 en
dc.identifier.issn 1469-8110 en
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:2027204
dc.description.abstract We address the problem of improving the efficiency of natural language text input under degraded conditions (for instance, on mobile computing devices or by disabled users), by taking advantage of the informational redundancy in natural language. Previous approaches to this problem have been based on the idea of prediction of the text, but these require the user to take overt action to verify or select the system’s predictions. We propose taking advantage of the duality between prediction and compression. We allow the user to enter text in compressed form, in particular, using a simple stipulated abbreviation method that reduces characters by 26.4%, yet is simple enough that it can be learned easily and generated relatively fluently. We decode the abbreviated text using a statistical generative model of abbreviation, with a residual word error rate of 3.3%. The chief component of this model is an n-gram language model. Because the system’s operation is completely independent from the user’s, the overhead from cognitive task switching and attending to the system’s actions online is eliminated, opening up the possibility that the compression-based method can achieve text input efficiency improvements where the prediction-based methods have not. We report the results of a user study evaluating this method. en
dc.description.sponsorship Engineering and Applied Sciences en
dc.language.iso en_US en
dc.publisher Cambridge University Press en
dc.relation.isversionof http://dx.doi.org/10.1017/S1351324906004311 en
dash.license LAA
dc.subject language modeling en
dc.subject natural language engineering en
dc.subject computer science en
dc.title Abbreviated text input using language modeling. en
dc.relation.journal Natural Language Engineering en
dash.depositing.author Shieber, Stuart

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  • FAS Scholarly Articles [7594]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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