Leveraging shortest dependency paths in low-resource biomedical relation extraction.

Saman Enayati, Slobodan Vucetic

Abstract read

In one paragraph

Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 · What the graph read from it

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1 citing paper in PubMed.

A framework for extraction of clinical information from radiological mammography reports using large language models and retrieval augmented generation.BMC medical informatics and decision making · 2026
Article

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5 · Who and what money

Authors and funding

2 authors.

Saman EnayatiDepartment of Computer and Information Sciences, Temple University, 1925 N. 12th Street, Suite 304, Philadelphia, PA, 19122, USA. samanenayati@temple.edu.

Slobodan VuceticDepartment of Computer and Information Sciences, Temple University, 1925 N. 12th Street, Suite 304, Philadelphia, PA, 19122, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBiomedical Relation Extraction (RE) is essential for uncovering complex relationships between biomedical entities within text. However, training RE classifiers is challenging in low-resource biomedical applications with few labeled examples.

methodsWe explore the potential of Shortest Dependency Paths (SDPs) to aid biomedical RE, especially in situations with limited labeled examples. In this study, we suggest various approaches to employ SDPs when creating word and sentence representations under supervised, semi-supervised, and in-context-learning settings.

resultsThrough experiments on three benchmark biomedical text datasets, we find that incorporating SDP-based representations enhances the performance of RE classifiers. The improvement is especially notable when working with small amounts of labeled data.

conclusionSDPs offer valuable insights into the complex sentence structure found in many biomedical text passages. Our study introduces several straightforward techniques that, as demonstrated experimentally, effectively enhance the accuracy of RE classifiers.

Indexed as

Data MiningNatural Language ProcessingHumansMachine LearningBERTLow-resourceNatural language processingRelation extractionShortest dependency path

Identifiers

PMID39049015

DOI10.1186/s12911-024-02592-2

PMCPMC11267752

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.