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In order to answer question, we propose a matching algorithm that consists in generating and learning inferences needed to rely text passages to pairs (question, candidate answer). We first retrieve relevant passages, through lexical expansion involving WordNet and word vectors, that are enriches by lexico-semantic resources. Then a tree edit model is used on graph representations of the passages and answer choices to extract edit sequences. Finally, features are computed from those edit sequences and used in two machine-learned models, one for validating answers and one for invalidating answers, in order to take the final decision. This method was evaluated in the Entrance Exams task at QALD@CLEF, that consists in answering QCMs on given texts, yielding the second best precision score on the task.

Apprentissage d'inférences par édition d'arbres pour répondre à des questions