Managing conflicts between users in Wikipedia

Wikipedia is nowadays a widely used encyclopedia, and one of the most visible sites on the Internet. Its strong principle of collaborative work and free editing sometimes generates disputes due to disagreements between users. In this article we study how the wikipedian community resolves the conflicts and which roles do wikipedian choose in this process. We observed the users behavior both in the article talk pages, and in the Arbitration Committee pages specifically dedicated to serious disputes. We first set up a users typology according to their involvement in conflicts and their publishing and management activity in the encyclopedia. We then used those user types to describe users behavior in contributing to articles that are tagged by the wikipedian community as being in conflict with the official guidelines of Wikipedia, or conversely as being well featured.Comment: 12 p

Coping with Alternate Formulations of Questions and Answers

We present in this chapter the QALC system which has participated in the four TREC QA evaluations. We focus here on the problem of linguistic variation in order to be able to relate questions and answers. We present first, variation at the term level which consists in retrieving questions terms in document sentences even if morphologic, syntactic or semantic variations alter them. Our second subject matter concerns variation at the sentence level that we handle as different partial reformulations of questions. Questions are associated with extraction patterns based on the question syntactic type and the object that is under query. We present the whole system thus allowing situating how QALC deals with variation, and different evaluations

How NLP Can Improve Question Answering

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 both to minimize the number of documents selected and to provide additional clues when comparing question and sentence representations. This comparison process also makes use of the results of a syntactic parsing of the questions and Named Entity recognition functionalities. Answer extraction relies on the application of syntactic patterns chosen according to the kind of information that is sought for, and categorized depending on the syntactic form of the question. These patterns allow QALC to handle nicely linguistic variations at the answer leve

Getting reliable answers by exploiting results from several sources of information

International audienceA question-answering system will be more convincing if it can give the user elements concerning the reliability of its propositions. In order to address this problem, we chose to take the advice of several searches. First, we search for answers in a reliable document collection, and second, on the Web. When both sources of knowledge allow the system to find common answers, we are confident with it and boost them at the first places

Trouver des réponses dans le web et dans une collection fermée

National audienceThe task of question answering, as defined in the TREC-11 evaluation, may rely on a Web search. However, this strategy is not a sufficient one, since Web results are not certified. Our system, QALC, searches both the Web and the AQUAINT text base. This implies that the system exists in two versions, each one of them dealing with one kind of resource. Particularly, Web requests may be extremely precise, and still be successful. Relying upon both kinds of search results yields a better ranking of the answers, hence a better functioning of the QALC system.La tâche de réponse à des questions, comme elle se présente dans le cadre de l'évaluation TREC-11, peut déclencher une recherche de la réponse en question sur le Web. Mais cette stratégie, à elle seule, ne garantit pas une bonne fiabilité de la réponse. Notre système, QALC, effectue donc une double recherche, sur le Web et sur la collection de référence AQUAINT. Cela suppose d'avoir deux versions du système, adaptées à ces deux ressources documentaires. En particulier, le Web peut être interrogé avec succès en gardant la question sous une forme extrêmement précise. Le fait de s'appuyer sur des résultats communs à ces deux recherches permet de mieux classer les réponses, et donc d'améliorer la performance du système QALC

Terminological variants for document selection and question/answer

information retrieva