Arnaud Grappy


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Methods Combination and ML-based Re-ranking of Multiple Hypothesis for Question-Answering Systems
Arnaud Grappy | Brigitte Grau | Sophie Rosset
Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual Data


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A Corpus for Studying Full Answer Justification
Arnaud Grappy | Brigitte Grau | Olivier Ferret | Cyril Grouin | Véronique Moriceau | Isabelle Robba | Xavier Tannier | Anne Vilnat | Vincent Barbier
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Question answering (QA) systems aim at retrieving precise information from a large collection of documents. To be considered as reliable by users, a QA system must provide elements to evaluate the answer. This notion of answer justification can also be useful when developping a QA system in order to give criteria for selecting correct answers. An answer justification can be found in a sentence, a passage made of several consecutive sentences or several passages of a document or several documents. Thus, we are interesting in pinpointing the set of information that allows to verify the correctness of the answer in a candidate passage and the question elements that are missing in this passage. Moreover, the relevant information is often given in texts in a different form from the question form: anaphora, paraphrases, synonyms. In order to have a better idea of the importance of all the phenomena we underlined, and to provide enough examples at the QA developer's disposal to study them, we decided to build an annotated corpus.