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New PubMedCausal Corpus Enhances Biomedical Causal Relation Extraction

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced PubMedCausal, a new corpus designed for causal relation extraction in biomedical text. This dataset, derived from PubMed abstracts, offers span-level annotations for 3,945 causal rows and 6,491 cause-effect pairs, enabling detailed evaluation of model capabilities. Benchmarks show that while biomedical encoders like PubMedBERT perform strongly in causal detection, generative models such as DeepSeek-R1-32B achieve competitive results in span-level extraction with few-shot prompting. AI

IMPACT This corpus will enable more precise evaluation of AI models in understanding complex causal relationships within biomedical literature.

RANK_REASON The cluster describes a new academic paper introducing a novel annotated corpus for a specific NLP task in the biomedical domain.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New PubMedCausal Corpus Enhances Biomedical Causal Relation Extraction

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The cluster describes a new academic paper introducing a novel annotated corpus for a specific NLP task in the biomedical domain.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Ifeoluwa Kunle-John, Josiah Paul, Oluwatosin Agbaakin, Peter Aina, Ikenna Odezuligbo, Sydney Anuyah ·

    PubMedCausal: A Span-Level Annotated Corpus for Causal Relation Extraction in Biomedical Text

    arXiv:2605.28363v1 Announce Type: new Abstract: 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 explicit caus…

  2. arXiv cs.CL TIER_1 English(EN) · Sydney Anuyah ·

    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 explicit causal cues. This limits their usefulness for evalua…

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