Building a Corpus for Biomedical Relation Extraction of Species Mentions
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Abstract (arXiv)
We present a manually annotated corpus, Species-Species Interaction, for extracting meaningful binary relations between species, in biomedical texts, at sentence level, with a focus on the gut microbiota. The corpus leverages PubTator to annotate species in full-text articles after evaluating different Named Entity Recognition species taggers. Our first results are promising for extracting relations between species using BERT and its biomedical variants.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 14 Jun 2023.
Builds on
6 of 21 references.
References · 6
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2017Inductive Representation Learning on Large Graphscited by 6,320
- 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
- 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,414
- 2019ScispaCy: Fast and Robust Models for Biomedical Natural Language Processingcited by 244
- 2022LinkBERT: Pretraining Language Models with Document Linkscited by 211
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Citing papers per year: 2024: 1; 2025: 1.
2 papers.