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.- Anthology ID:
- C16-1239
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 2537–2547
- Language:
- URL:
- https://aclanthology.org/C16-1239
- DOI:
- Cite (ACL):
- Pankaj Gupta, Hinrich Schütze, and Bernt Andrassy. 2016. Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2537–2547, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Table Filling Multi-Task Recurrent Neural Network for Joint Entity and Relation Extraction (Gupta et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/C16-1239.pdf
- Code
- pgcool/TF-MTRNN