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Academic Paper

Title: Probabilistic and Possibilistic Language Models Based on the World Wide Web
Paper URL:
Author: Stanislas Oger
Institution: University of Avignon
Author: Georges Linarès
Institution: University of Avignon
Linguistic Field: Computational Linguistics; Text/Corpus Linguistics
Abstract: Usually, language models are built either from a closed corpus, or by using World Wide Web retrieved documents, which are considered as a closed corpus themselves. In this paper we propose several other ways, more adapted to the nature of the Web, of using this resource for language modeling. We first start by improving an approach consisting in estimating n-gram probabilities from Web search engine statistics. Then, we propose a new way of considering the information extracted from the Web in a probabilistic framework. Then, we also propose to rely on Possibility Theory for effectively using this kind of information. We compare these two approaches on two automatic speech recognition tasks: (i) transcribing broadcast news data, and (ii) transcribing domain-specific data, concerning surgical operation film comments. We show that the two approaches are effective in different situations.
Type: Individual Paper
Status: Completed
Venue: International Speech Communication Association (ISCA)
Publication Info: Proceedings of the 10th Annual Conference of the International Speech Communication Association (InterSpeech)

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