Learn More
Answering to precise questions requires applying Natural Language techniques in order to locate the answers inside retrieved documents. The QALC system, presented in this paper, participated t o the Question Answering track of the TREC8 and TREC9 evaluations. QALC exploits an analysis of documents based on the search for multi-word terms and their(More)
The increasing amount of available textual information makes necessary the use of Natural Language Processing (NLP) tools. These tools have to be used on large collections of documents in different languages. But NLP is a complex task that relies on many processes and resources. As a consequence, NLP tools must be both configurable and efficient: specific(More)
Information Extraction has recently been extended to new areas by loosening the constraints on the strict definition of the extracted information and allowing to design more open information extraction systems. In this new domain of unsupervised information extraction, we focus on the task of extracting and characterizing <i>a priori</i> unknown relations(More)
A good dictionary contains not only many entries and a lot of information concerning each one of them, but also adequate means to reveal the stored information. Information access depends crucially on the quality of the index. We will present here some ideas of how a dictionary could be enhanced to support a speaker/writer to find the word s/he is looking(More)
Answering open-domain factual questions requires Natural Language processing for refining document selection and answer identification. With our system QALC, we have participated to the Question Answering track of the TREC8, TREC9 and TREC10 evaluations. QALC performs an analysis of documents relying on multi-word term search and their linguistic variation(More)
In this article, we present a method for extracting automatically from texts semantic relations in the medical domain using linguistic patterns. These patterns refer to three levels of information about words: inflected form, lemma and part-of-speech. The method we present consists first in identifying the entities that are part of the relations to extract,(More)