Toward Robust Information Extraction Models for Multimedia Documents
type: Dataset
description: During the last decade, huge amounts of multimedia documents have been generated. It is therefore important to find a way to manage this data. Every approach to facilitate this process requires to have a deep understanding of the content of the documents. Among two different approaches to get such insights, either by extracting information from the document (e.g. audio, image) or by using related data from external sources (such as the Web), we chose the latter. Then, these extracted information can be used in a global framework to be considered as annotations for multimedia documents in order to facilitate the management of such documents. One of the main objectives of this thesis was to be robust against noisy and small data. Our approach to reach this objective was to use simple and knowledge-light techniques (i.e. shallow linguistic analysis) as a guarantee of robustness that we assume to be mandatory for processing multimedia documents. Indeed, we used statistical analysis of text and some techniques inspired from Information Retrieval. In addition, we introduced a new data representation scheme for text processing which has been used successfully in image Information Retrieval domain. In this thesis, we focused on three tasks: Relation Extraction, Relation Discovery and Proper noun clustering. In the first task, Relation Extraction, we proposed a supervised model based on a Language Modeling and an instance-based learning algorithm, called kNN. Experimental results showed the effectiveness of our models which use shallow linguistic information compared to state-of-the-art systems that use deep linguistic analysis. In the second task, we moved to unsupervised model to discover relations instead of extracting predefined ones. We modeled this problem as clustering task and defined a similarity function based on Language Modeling and average probability. The performance of this model was evaluated with a textual football reports, which showed improvements compare …
landing page: https://theses.hal.science/tel-00760383
keyword: bag-of-vectors
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-clcomputer-science-cscomputation-and-language-cscl
keyword: infoinfo-ircomputer-science-csinformation-retrieval-csir
keyword: information-extraction
keyword: language-modeling
keyword: n-gram
keyword: proper-noun-clustering
keyword: relation-discovery
keyword: relation-extraction
keyword: semantic-clustering
keyword: similarity-function
keyword: theses
issued: 2026-06-02T16:43:52.970145
modified: 2026-06-02T16:43:52.970161
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Toward Robust Information Extraction Models for Multimedia Documents
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00760383
issued: 2026-06-02T16:43:53.092380
modified: 2026-06-02T16:43:52.901377