arXiv:2106.01793
Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction
원문 초록
Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences. Yet, human annotators usually use a small number of sentences to identify the relationship between a given entity pair. In this paper, we present an embarrassingly simple but effective method to heuristically select evidence sentences for document-level RE, which can be easily combined with BiLSTM to achieve good performance on benchmark datasets, even better than fancy graph neural network based methods. We have released our code at https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Improving Document-level Relation Extraction via Contextualizing Mention Representations and Weighting Mention Pairs
Chaining Event Spans for Temporal Relation Grounding
Comparative Analysis of AI Agent Architectures for Entity Relationship Classification
Towards a More Generalized Approach in Open Relation Extraction
Towards Rehearsal-Free Continual Relation Extraction: Capturing Within-Task Variance with Adaptive Prompting
Bridging Generative and Discriminative Learning: Few-Shot Relation Extraction via Two-Stage Knowledge-Guided Pre-training
토론
이 논문과 연결된 포럼 글이 아직 없습니다.