Span-based Joint Entity and Relation Extraction with Transformer Pre-training

Code

• github.com/markus-eberts/spert
  OfficialIn paperpytorch★ 0
• github.com/yangyucheng000/Paper-3/tree/main/SpanQualifier
  mindspore★ 0
• github.com/lavis-nlp/spert
  pytorch★ 0

Abstract

We introduce SpERT, an attention model for span-based joint entity and relation extraction. Our key contribution is a light-weight reasoning on BERT embeddings, which features entity recognition and filtering, as well as relation classification with a localized, marker-free context representation. The model is trained using strong within-sentence negative samples, which are efficiently extracted in a single BERT pass. These aspects facilitate a search over all spans in the sentence. In ablation studies, we demonstrate the benefits of pre-training, strong negative sampling and localized context. Our model outperforms prior work by up to 2.6% F1 score on several datasets for joint entity and relation extraction.

Tasks

Benchmark Results

Reproductions