Ludovic Bonnefoy

The Web as a Source of Evidence for Filtering Candidate Answers to Natural Language Questions

Identifying and extracting named entities from web pages has been the subject of many researches. In this paper, we propose and evaluate some new unsupervised language modeling approaches to determine the membership level of a candidate answer, a named entity, to a natural language question to a very fine-grained conceptual class of entity. We propose to address this issue by using the Web or DBPedia hierarchy as sources of evidence. Then, this level of membership can be used to improve the ranking of candidate answers in a question-answering task. Lastly, we present the results we obtained by participating in TREC 2010 Entity track.

Large Scale Text Mining Approaches for Information Retrieval and Extraction

The issues for Natural Language Processing and Information Retrieval have been studied for long time but the recent availability of very large resources (Web pages, digital documents…) and the development of statistical machine learning methods exploiting annotated texts (manual encoding by crowdsourcing is a new major way) have transformed these fields. This allows not limiting these approaches to highly specialized domains and reducing the cost of their implementation. For this chapter, our aim is to present some popular text-mining statistical approaches for information retrieval and information extraction and to discuss the practical limits of actual systems that introduce challenges for future.