Methods to integrate a language model with semantic information for a word prediction component

Most current word prediction systems make use of n-gram language models (LM) to estimate the probability of the following word in a phrase. In the past years there have been many attempts to enrich such language models with further syntactic or semantic information. We want to explore the predictive powers of Latent Semantic Analysis (LSA), a method that has been shown to provide reliable information on long-distance semantic dependencies between words in a context. We present and evaluate here several methods that integrate LSA-based information with a standard language model: a semantic cache, partial reranking, and different forms of interpolation. We found that all methods show significant improvements, compared to the 4-gram baseline, and most of them to a simple cache model as well.Comment: 10 pages ; EMNLP'2007 Conference (Prague

Prédiction de mots et saisie de requêtes sur interfaces limitées : dispositifs mobiles et aide au handicap

chapitre 10Ce chapitre fait le tour de la question des techniques de prédiction lexicale utilisées aussi bien dans les systèmes d'aide à la communication pour personnes handicapées que dans les systèmes d'aide à la saisie de texte sur dispositifs limités tels que les téléphones mobiles

Proceedings of the First European Workshop on Latent Semantic Analysis in Technology Enhanced Learning

Latent Semantic Analysis (LSA) has been successfully deployed in various educational applications to enrich learning and teaching with information-technology. The primary goal of the workshop is to bring together experts in the field in order to share knowledge gained within the scattered research about latent semantic analysis in educational applications, in particular from the context of the IST projects Cooper, iCamp,T enCompetence and ProLearn

Proceedings of the First European Workshop on Latent Semantic Analysis in Technology Enhanced Learning

Latent Semantic Analysis (LSA) has been successfully deployed in various educational applications to enrich learning and teaching with information-technology. The primary goal of the workshop is to bring together experts in the field in order to share knowledge gained within the scattered research about latent semantic analysis in educational applications, in particular from the context of the IST projects Cooper, iCamp,T enCompetence and ProLearn

The language component of the FASTY text prediction system

This paper describes the language component of FASTY, a text prediction system designed to improve text input efficiency for disabled users. The FASTY language component is based on state-of-the-art n-gram-based word-level and part-of-speech-level prediction and on a number of innovative modules (morphological analysis, collocation-based prediction, compound prediction) that are meant to enhance performance in languages other than English. Together with its modular architecture, these novel techniques make it adaptable to a wide range of languages without sacrificing performance. Currently, versions for Dutch, German, French, Italian, and Swedish are supported. The system can be parameterized to be used with different user interfaces and for a range of different applications. In this paper, we discuss each of the modules in detail and we present a series of experimental evaluations of the system