# Building a Corpus for Biomedical Relation Extraction of Species Mentions

arXiv: https://arxiv.org/abs/2306.08403 · HTML: https://mlsanity.com/paper/2306.08403/

## 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 (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [LinkBERT: Pretraining Language Models with Document Links](https://mlsanity.com/paper/2203.15827/) (2022)

## Built on by (most cited first, 2 of 2)

- [Summarization for Generative Relation Extraction in the Microbiome Domain](https://mlsanity.com/paper/2506.08647/) (2025)
- [CO-Fun: A German Dataset on Company Outsourcing in Fund Prospectuses for Named Entity Recognition and Relation Extraction](https://mlsanity.com/paper/2403.15322/) (2024)

## 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.