FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering
Year: 2017
Type: conference-paper
Abstract: 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.
DOI: 10.18653/v1/s17-2048
Authors: Giuseppe Attardi, Antonio Carta, Federico Errica, Andrea Madotto, Ludovica Pannitto
Institution: University of Pisa
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2751642587