Interactive Text Generation

  • Alejandro Héctor Toselli
  • Enrique Vidal
  • Francisco Casacuberta


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.


Input Pattern User Feedback Greedy Approach Word Fragment Optimal Decision Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Alejandro Héctor Toselli
    • 1
  • Enrique Vidal
    • 1
  • Francisco Casacuberta
    • 1
  1. 1.Instituto Tecnológico de InformáticaUniversidad Politécnica de ValenciaValenciaSpain

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