Publishing Partner: Cambridge University Press CUP Extra Publisher Login
amazon logo
More Info


New from Oxford University Press!

ad

Linguistic Diversity and Social Justice

By Ingrid Piller

Linguistic Diversity and Social Justice "prompts thinking about linguistic disadvantage as a form of structural disadvantage that needs to be recognized and taken seriously."


New from Cambridge University Press!

ad

Language Evolution: The Windows Approach

By Rudolf Botha

Language Evolution: The Windows Approach addresses the question: "How can we unravel the evolution of language, given that there is no direct evidence about it?"


The LINGUIST List is dedicated to providing information on language and language analysis, and to providing the discipline of linguistics with the infrastructure necessary to function in the digital world. LINGUIST is a free resource, run by linguistics students and faculty, and supported primarily by your donations. Please support LINGUIST List during the 2016 Fund Drive.

Academic Paper


Title: Probabilistic and Possibilistic Language Models Based on the World Wide Web
Paper URL: http://www.isca-speech.org/archive/interspeech_2009/i09_2699.html
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)
URL: http://www.isca-speech.org/archive/interspeech_2009/i09_2699.html


Add a new paper
Return to Academic Papers main page
Return to Directory of Linguists main page