Sanaphor++: Combining Deep Neural Networks with Semantics for Coreference Resolution

Julien Plu (1) , Roman Prokofyev (2) , Alberto Tonon (2) , Philippe Cudré-Mauroux (2) , Djellel Eddine Difallah (2) , Raphaël Troncy (1) , Giuseppe Rizzo (3)

1 Eurecom [Sophia Antipolis]
2 eXascale Infolab [Fribourg]
3 ISMB - Istituto Superiore Mario Boella

Coreference resolution has always been a challenging task in Natural Language Processing. Machine learning and semantic techniques have improved the state of the art over the time, though since a few years, the biggest step forward has been made using deep neural networks. In this paper, we describe Sanaphor++, which is an improvement of a top-level deep neural network system for coreference resolution-namely Stanford deep-coref-through the addition of semantic features. The goal of Sanaphor++ is to improve the clustering part of the coreference resolution in order to know if two clusters have to be merged or not once the pairs of mentions have been identified. We evaluate our model over the CoNLL 2012 Shared Task dataset and compare it with the state-of-the-art system (Stanford deep-coref) where we demonstrated an average gain of 1.13% of the average F1 score.

Domaines: Ingénierie assistée par ordinateur

Julien Plu, Roman Prokofyev, Alberto Tonon, Philippe Cudré-Mauroux, Djellel Eddine Difallah, et al.. Sanaphor++: Combining Deep Neural Networks with Semantics for Coreference Resolution. LREC 2018, 11th Language Resources and Evaluation Conference, May 2018, Miyazaki, Japan. ⟨hal-03577175⟩