Abstract
We demonstrate that for sentence-level relation extraction it is beneficial to consider other relations in the sentential context while predicting the target relation. Our architecture uses an LSTM-based encoder to jointly learn representations for all relations in a single sentence. We combine the context representations with an attention mechanism to make the final prediction. We use the Wikidata knowledge base to construct a dataset of multiple relations per sentence and to evaluate our approach. Compared to a baseline system, our method results in an average error reduction of 24 on a held-out set of relations. The code and the dataset to replicate the experiments are made available at https://github.com/ukplab/.- Anthology ID:
- D17-1188
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1784–1789
- Language:
- URL:
- https://aclanthology.org/D17-1188
- DOI:
- 10.18653/v1/D17-1188
- Cite (ACL):
- Daniil Sorokin and Iryna Gurevych. 2017. Context-Aware Representations for Knowledge Base Relation Extraction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1784–1789, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Context-Aware Representations for Knowledge Base Relation Extraction (Sorokin & Gurevych, EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D17-1188.pdf