Does constituency analysis enhance domain-specific pre-trained BERT models for relation extraction?
원문 초록
Recently many studies have been conducted on the topic of relation extraction. The DrugProt track at BioCreative VII provides a manually-annotated corpus for the purpose of the development and evaluation of relation extraction systems, in which interactions between chemicals and genes are studied. We describe the ensemble system that we used for our submission, which combines predictions of fine-tuned bioBERT, sciBERT and const-bioBERT models by majority voting. We specifically tested the contribution of syntactic information to relation extraction with BERT. We observed that adding constituentbased syntactic information to BERT improved precision, but decreased recall, since relations rarely seen in the train set were less likely to be predicted by BERT models in which the syntactic information is infused. Our code is available online [https://github.com/Maple177/drugprot-relation-extraction].
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
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
Relation Extraction or Pattern Matching? Unravelling the Generalisation Limits of Language Models for Biographical RE
토론
이 논문과 연결된 포럼 글이 아직 없습니다.