Section: Research Program

Corpus-based text description and machine learning

Our work on textual material (textual documents, transcriptions of speech documents, captions in images or videos, etc.) is characterized by a chiefly corpus-based approach, as opposed to an introspective one. A corpus is for us a huge collection of textual documents, gathered or used for a precise objective. We thus exploit specialized (abstracts of biomedical articles, computer science texts, etc.) or non specialized (newspapers, broadcast news, etc.) collections for our various studies. In TexMex , according to our applications, different kinds of knowledge can be extracted from the textual material. For example, we automatically extract terms characteristic of each successive topic in a corpus with no a priori knowledge; we produce representations for documents in an indexing perspective  [95] ; we acquire lexical resources from the collections (morphological families, semantic relations, translation equivalences, etc.) in order to better grasp relations between segments of texts in which a same idea is expressed with different terms or in different languages...

In the domain of the corpus-based text processing, many researches have been undergone in the last decade. While most of them are essentially based on statistical methods, symbolic approaches also present a growing interest  [82] . For our various problems involving language processing, we use both approaches, making the most of existing machine learning techniques or proposing new ones. Relying on advantages of both methods, we aim at developing machine learning solutions that are automatic and generic enough to make it possible to extract, from a corpus, the kind of elements required by a given task.