Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction

Code

• github.com/pgcool/TF-MTRNN
  OfficialIn papertf★ 0

Abstract

This paper proposes a novel context-aware joint entity and word-level relation extraction approach through semantic composition of words, introducing a Table Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classification tasks to a table-filling problem and models their interdependencies. The proposed neural network architecture is capable of modeling multiple relation instances without knowing the corresponding relation arguments in a sentence. The experimental results show that a simple approach of piggybacking candidate entities to model the label dependencies from relations to entities improves performance. We present state-of-the-art results with improvements of 2.0\% and 2.7\% for entity recognition and relation classification, respectively on CoNLL04 dataset.

Tasks

Reproductions