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 AttardiCo-primo
;Antonio CartaCo-primo
;Andrea MadottoCo-primo
;Ludovica PannittoCo-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.File in questo prodotto:
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