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arXiv Computation and Language By 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

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PubMedCausal is a new span‑level annotated corpus for causal relation extraction in biomedical text, comprising 30,000 paragraph‑level rows with 3,945 causal rows and 6,491 adjudicated cause–effect pairs. Each causal relation is annotated with full‑text cause and effect spans, causality type, and sententiality, allowing evaluation of both causal detection and full‑span extraction. Benchmarking shows biomedical encoders like PubMedBERT achieve an F1 of 0.7391 for detection, while the generative model DeepSeek‑R1‑32B reaches a Cosine Pair F1 of 0.6765 for extraction, and the corpus supports cross‑dataset transfer learning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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