Information Digestion

Gaël Dias 1
1 hultech
LIFO - Laboratoire d'Informatique Fondamentale d'Orléans
Abstract : The World Wide Web (WWW) is a huge information network within which searching for relevant quality contents remains an open question. The ambiguity of natural language is traditionally one of the main reasons, which prevents search engines from retrieving information according to users' needs. However, the globalized access to the WWW via Weblogs or social networks has highlighted new problems. Web documents tend to be subjective, they mainly refer to actual events to the detriment of past events and their ever growing number contributes to the well-known problem of information overload. In this thesis, we present our contributions to digest information in real-world heterogeneous text environments (i.e. the Web) thus leveraging users' efforts to encounter relevant quality information. However, most of the works related to Information Digestion deal with the English language fostered by freely available linguistic tools and resources, and as such, cannot be directly replicated for other languages. To overcome this drawback, two directions may be followed: on the one hand, building resources and tools for a given language, or on the other hand, proposing language-independent approaches. Within the context of this report, we will focus on presenting language-independent unsupervised methodologies to (1) extract implicit knowledge about the language and (2) understand the explicit information conveyed by real-world texts, thus allowing to reach Multilingual Information Digestion.
Document type :
Habilitation à diriger des recherches
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Submitted on : Monday, February 13, 2012 - 7:31:39 PM
Last modification on : Thursday, January 17, 2019 - 3:06:06 PM
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  • HAL Id : tel-00669780, version 1

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Gaël Dias. Information Digestion. Machine Learning [cs.LG]. Université d'Orléans, 2010. ⟨tel-00669780⟩

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