Corpus-based stochastic finite-state predictive text entry for reduced keyboards : application to Catalan

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/1783
Información del item - Informació de l'item - Item information
Title: Corpus-based stochastic finite-state predictive text entry for reduced keyboards : application to Catalan
Authors: Forcada, Mikel L.
Keywords: Corpus-based | Stochastic finite-state predictive text | Reduced keyboards | Catalan language
Issue Date: Sep-2001
Publisher: Sociedad Española para el Procesamiento del Lenguaje Natural
Citation: FORCADA ZUBIZARRETA, Mikel L. “Corpus-based stochastic finite-state predictive text entry for reduced keyboards : application to Catalan”. Procesamiento del lenguaje natural. Nº 27 (sept. 2001), pp. 65-70
Abstract: Users of digital mobile phones have access to an increasing number of text-based services but most of them can only use a very reduced keyword for text entry. Traditionally, this has been solved by means of multiple tapping and delays or next-character keys (e.g. 66(pause)666 for no), which is slow (two taps per letter on average), inconvenient, and prone to error. Recently, some mobile terminals offer dictionary-based predictive text-entry schemes which, in almost all cases, allow for one-tap-per-letter text entry (e.g. 367 for for). This paper shows how text corpora can be used to build stochastic finite-state automata which may in turn be easily used to implement predictive text entry for medium-sized languages such as Catalan, not currently supported by major predictive-text-entry companies.
Sponsor: Support from the Spanish Comisión Interministerial de Ciencia y Tecnología through grants TIC97-0941 and TIC2000-1599-C02-02 is acknowledged.
URI: http://hdl.handle.net/10045/1783
ISSN: 1135-5948
Language: eng
Type: info:eu-repo/semantics/article
Appears in Collections:Procesamiento del Lenguaje Natural - Nº 27 (septiembre 2001)
INV - TRANSDUCENS - Artículos de Revistas

Files in This Item:
Files in This Item:
File Description SizeFormat 
ThumbnailPLN_27_07.pdf200,58 kBAdobe PDFOpen Preview


Items in RUA are protected by copyright, with all rights reserved, unless otherwise indicated.