Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest
Medical Relation Extraction (MRE) task aims to extract relations between entities in medical texts. Traditional relation extraction methods achieve impressive success by exploring the syntactic information, e.g., dependency tree. However, the quality of the 1-best dependency tree for medical texts produced by an out-of-domain parser is relatively limited so that the performance of medical relation extraction method may degenerate. To this end, we propose a method to jointly model semantic and syntactic information from medical texts based on causal explanation theory. We generate dependency forests consisting of the semantic-embedded 1-best dependency tree. Then, a task-specific causal explainer is adopted to prune the dependency forests, which are further fed into a designed graph convolutional network to learn the corresponding representation for downstream task. Empirically, the various comparisons on benchmark medical datasets demonstrate the effectiveness of our model.
Code (1)
Tasks
Medical Relation ExtractionRelationRelation ExtractionSimilar Papers 제목 키워드 기반
PubMedCausal: A Span-Level Annotated Corpus for Causal Relation Extraction in Biomedical Text
Causal relation extraction (CRE) is central to biomedical text mining, but current resources often conflate causal relations with broader associations, restrict annotation to sentence-level examples, or focus mainly on e…
Relation ExtractionTransfer LearningInformative Causality Extraction from Medical Literature via Dependency-tree based Patterns
Extracting cause-effect entities from medical literature is an important task in medical information retrieval. A solution for solving this task can be used for compilation of various causality relations, such as, causal…
ArticlesInformation RetrievalRetrievalSentenceCausality extraction from medical text using Large Language Models (LLMs)
This study explores the potential of natural language models, including large language models, to extract causal relations from medical texts, specifically from Clinical Practice Guidelines (CPGs). The outcomes causality…
BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction
Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current knowledge we have about those entities a…
Deep LearningRelationRelation ExtractionTransfer Learning for Causal Sentence Detection
We consider the task of detecting sentences that express causality, as a step towards mining causal relations from texts. To bypass the scarcity of causal instances in relation extraction datasets, we exploit transfer le…
RelationRelation ExtractionSentenceTransfer Learning