Enriching Pre-trained Language Model with Entity Information for Relation Classification

Code

• github.com/onehaitao/R-BERT-relation-extraction
  pytorch★ 9
• github.com/chielingyueh/anaphora_resolution_chemical_patents
  pytorch★ 1
• github.com/mickeystroller/R-BERT
  pytorch★ 0
• github.com/wang-h/bert-relation-classification
  pytorch★ 0
• github.com/Valdegg/anlp_rbert
  none★ 0
• github.com/monologg/R-BERT
  pytorch★ 0

Abstract

Relation classification is an important NLP task to extract relations between entities. The state-of-the-art methods for relation classification are primarily based on Convolutional or Recurrent Neural Networks. Recently, the pre-trained BERT model achieves very successful results in many NLP classification / sequence labeling tasks. Relation classification differs from those tasks in that it relies on information of both the sentence and the two target entities. In this paper, we propose a model that both leverages the pre-trained BERT language model and incorporates information from the target entities to tackle the relation classification task. We locate the target entities and transfer the information through the pre-trained architecture and incorporate the corresponding encoding of the two entities. We achieve significant improvement over the state-of-the-art method on the SemEval-2010 task 8 relational dataset.

Tasks

Benchmark Results

Reproductions