Weitere Kapitel dieses Buchs durch Wischen aufrufen
Using a computer to produce text documents is essentially a manual task nowadays. The computer is basically seen as an electronic typewriter and all the effort required falls on the human user who has to, firstly, think of a grammatically and semantically correct piece of text and, then, type on the computer. Although human beings are usually quite efficient when performing this task, in some cases, this process can be very time consuming. Writing text in a non-native language, using devices having highly constrained input interfaces, or the case of impaired people using computers are only a few examples. Providing some kind of automation in these scenarios could be really useful.
Interactive Text Prediction deals with providing assistance in document typing tasks. IPR techniques are used to predict what the user is going to type, given the text typed previously. Prediction is studied both at the word level and at the character level but, in both cases, the aim is to predict multi-word text chunks, not just a single next word or word fragment. Empirical tests suggest that significant amounts of user typing (and to some extent also thinking) effort can be saved using the proposed approaches. In this chapter, alternative strategies to perform the search in this type of tasks are also presented and discussed in detail.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Bickel, S., Haider, P., & Scheffer, T. (2005). Predicting sentences using n-gram language models. In HLT’05: Proceedings of the conference on human language technology and empirical methods in natural language processing (pp. 193–200), Morristown, NJ, USA. Menlo Park: Association for Computational Linguistics. CrossRef
Jelinek, F. (1998). Statistical methods for speech recognition. Cambridge: MIT Press.
Oncina, J. (2009). Optimum algorithm to minimize human interactions in sequential computer assisted pattern recognition. Pattern Recognition Letters, 30(6), 558–563. CrossRef
Trost, H., Matiasek, J., & Baroni, M. (2005). The language component of the fasty text prediction system. Applied Artificial Intelligence, 19(8), 743–781. CrossRef
- Interactive Text Generation
Dr. Alejandro Héctor Toselli
Dr. Enrique Vidal
Prof. Francisco Casacuberta
- Springer London
- Chapter 10