mlsanityData to 14 Oct 2026

Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest

arXiv 2208.13472 ↗First posted 29 Aug 2022cs.CLcited by 5

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 29 Aug 2022.

Builds on

17 of 36 references.

References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  4. 2018Graph Convolution over Pruned Dependency Trees Improves Relation Extractioncited by 146
  5. 2021Generative Causal Explanations for Graph Neural Networkscited by 124
  6. 2017Cross-Sentence N-ary Relation Extraction with Graph LSTMscited by 101
  7. 2019CXPlain: Causal Explanations for Model Interpretation under Uncertaintycited by 98
  8. 2019Attention Guided Graph Convolutional Networks for Relation Extractioncited by 82
  9. 2019Neural Network Attributions: A Causal Perspectivecited by 77
  10. 2016Distant Supervision for Relation Extraction beyond the Sentence Boundarycited by 62
  11. 2018Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extractioncited by 42
  12. 2019Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learningcited by 29
5 more
  1. 2018N-ary Relation Extraction using Graph State LSTMcited by 26
  2. 2020Modeling Topical Relevance for Multi-Turn Dialogue Generationcited by 15
  3. 2019A Silver Standard Corpus of Human Phenotype-Gene Relationscited by 13
  4. 2019Leveraging Dependency Forest for Neural Medical Relation Extractioncited by 10
  5. 2017Incorporating Syntactic Uncertainty in Neural Machine Translation with Forest-to-Sequence Modelcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 0; 2026: 2.

5 papers.

  1. 2023Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modelingcited by 16
  2. 2023Exploring Causal Learning through Graph Neural Networks: An In-depth Reviewcited by 8
  3. 2024Introducing Diminutive Causal Structure into Graph Representation Learningcited by 1
  4. 2026Applicability Condition Extraction for Therapeutic Drug-Disease Relationscited by 0
  5. 2026PiPMRE: A Pipeline Based on Language Model for Medical Relation Extractioncited by 0