In this paper we present ThReeNN, a model for Community Question Answer- ing, Task 3, of SemEval-2017. The pro- posed model exploits both syntactic and semantic information to build a single and meaningful embedding space. Using a de- pendency parser in combination with word embeddings, the model creates sequences of inputs for a Recurrent Neural Network, which are then used for the ranking pur- poses of the Task. The score obtained on the official test data shows promising re- sults.

FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering

Giuseppe Attardi
Co-primo
;
Antonio Carta
Co-primo
;
Andrea Madotto
Co-primo
;
Ludovica Pannitto
Co-primo
2017-01-01

Abstract

In this paper we present ThReeNN, a model for Community Question Answer- ing, Task 3, of SemEval-2017. The pro- posed model exploits both syntactic and semantic information to build a single and meaningful embedding space. Using a de- pendency parser in combination with word embeddings, the model creates sequences of inputs for a Recurrent Neural Network, which are then used for the ranking pur- poses of the Task. The score obtained on the official test data shows promising re- sults.
2017
978-1-945626-55-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/904374
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