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

2017-08-01 · SEMEVAL 2017 8 · Giuseppe Attardi, Antonio Carta, Federico Errica, Andrea Madotto, Ludovica Pannitto

In this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017. The proposed model exploits both syntactic and semantic information to build a single and meaningful embedding space. Using a dependency parser in combination with word embeddings, the model creates sequences of inputs for a Recurrent Neural Network, which are then used for the ranking purposes of the Task. The score obtained on the official test data shows promising results.

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Tasks

Community Question Answering Dependency ParsingModel SelectionQuestion Answering Question SimilarityWord Embeddings

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