Building a Corpus for Biomedical Relation Extraction of Species Mentions (2023)

Abstract: We present a manually annotated new corpus, Species-Species Interaction (SSI), 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 NER species taggers. Our first results are promising for extracting relations between species using BERT and its biomedical variants.

## introduction the field of biomedical relation extraction (re) has made significant advancements in recent years, with the development of various state-of-the-art models for extracting meaningful relationships between entities from scientific articles. however, the availability of annotated datasets for specific types of relations, such as interactions between species, remains limited. studying the interactions and relationships between different species is of great interest in many biomedical research areas, including gut microbiome research. the gut microbiota, a complex community of microorganisms residing in the human gastrointestinal tract, plays a crucial role in human health and disease. understanding the interactions and relationships between different species within the gut microbiome is of significant importance in advancing knowledge on microbiome-mediated health outcomes. the first challenge in studying this type of interactions is the lack of annotated datasets that specifically capture the semantic expressions of interactions between species. most studied interactions occur between proteins, drugs, genes, and diseases. the representations of these relations may not fully capture the complexity of species interactions, particularly in the specific context of the microbiome. in this article, we present a dedicated corpus for relation extraction of species mentions in biomedical texts, by manually annotating binary relations at the sentence level in an existing corpus for named entity recognition (ner) and in a new corpus focusing on the gut microbiome. we also choose to leverage pubtator, a reliable system for annotating species in full-text articles, after evaluating its performance with respect to a fine-tuned bert and a scispacy model. we carefully selected a set of pmc documents from pubtator based on heuristics that align with our domain of interest and annotated a selection of sentences from the corpus with binary relations, consisting of the presence/absence of a relation. afterwards, we proceeded to fine-tune existing transformer-based models on our corpus to highlight the impact of a new small set of semantic relation expressions. our contributions are as follows: ## relation extraction corpus for species the participating entities in known interactions are major biomedical concepts, such as genes, proteins, diseases, and drugs. as we focus on studying relations occurring between species in scientific research articles, we encountered significant challenges. one major challenge is the scarcity of annotated corpora that capture the nuanced semantic expressions of interactions between species, which are distinct from the well-studied re datasets. as a first step to tackle this problem, we introduced species-species interaction (ssi), a manually annotated corpus dedicated to species binary interactions, built from full-text scientific articles. ## experiments we first conducted experiments on ner taggers for the species mentions and then evaluated bert-like models for the re tasks on the ssi corpus. ## conclusion we introduced a new corpus called species-species interaction (ssi) for extracting meaningful binary relations between species in biomedical texts, with a focus on the gut microbiome, to address the absence of species-related corpora. we included a comparative study of species ner taggers followed by initial results on extracting relations using bert and its biomedical variants. our experiments demonstrated that performances of pretrained models were improved with fine-tuning, despite the use of a limited-size corpus. to gain a better understanding of these results, we performed an error analysis, allowing us to identify specific areas for improvement that should be taken into consideration. in the future, we will aim to enlarge our ssi corpus by further annotating sentences and also by integrating relation types to better represent species-species interactions.