LIMSI-CNRS@CLEF 2015: Tree Edit Beam Search for Multiple Choice Question Answering

This paper describes our participation to the Entrance Ex- ams Task of CLEF 2015's Question Answering Track. The goal is to an- swer multiple-choice questions on short texts. Our system rst retrieves passages relevant to the question, through lexical expansion involving WordNet and word vectors. Then a tree edit model is used on graph representations of the passages and answer choices to extract edit se- quences. Finally, features are computed from those edit sequences and used in various machine-learned models to take the nal decision. We submitted several runs in the task, one of which yielding a c@1 of 0.36, which makes our team the second best on the task.