Information Digestion
type: Dataset
contact point: Organization
  fn: CCSD
keyword: habilitation-a-diriger-des-recherches
keyword: real-world-text-environments
keyword: infoinfo-aicomputer-science-csartificial-intelligence-csai
keyword: word-semantic-relations
keyword: unsupervised-language-independent-approaches
keyword: sentiment-analysis
keyword: environnement-textuels
keyword: infoinfo-lgcomputer-science-csmachine-learning-cslg
keyword: explicit-and-implicit-knowledge-extraction
keyword: extraction-de-connaissances-explicites-et-implicites
keyword: information-digestion
keyword: relation-semantiques-entre-mots
keyword: methodes-non-supervisees-independantes-de-la-langue
keyword: analyse-de-sentiments
keyword: digestion-dinformation
keyword: infoeu-reposemanticsother
landing page: https://theses.hal.science/tel-00669780
description: 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.
issued: 2026-05-28T20:11:27.729896
language: en
modified: 2026-05-28T20:11:27.729902

test_moissonnage_selune
type: Agent

Information Digestion
type: Distribution
access URL: https://theses.hal.science/tel-00669780
format: HTML
issued: 2026-05-28T20:11:27.894666
modified: 2026-05-28T20:11:27.700445