Distinguishing between focus and background entities in biomedical corpora using discourse structure and transformers (2022)

Abstract: Scientific documents typically contain numerous entity mentions, while only a subset are directly relevant to the key contributions of the paper. Distinguishing these focus entities from background ones effectively could improve the recovery of relevant documents and the extraction of information from documents. To study the identification of focus entities, we developed two large datasets of disease-causing biological pathogens using MEDLINE, the largest collection of biomedical citations, and PubMed Central, a collection of full text articles. The focus entities were identified using human-curated indexing on these collections. Experiments with machine learning methods to identify focus entities show that transformer methods achieve high precision and recall and that document discourse information is relevant. The work lays the foundation for more targeted retrieval/summarisation of entity-relevant documents.

## introduction scientific documents typically discuss one or more topics linked to key entities of interest. however, entities may also be mentioned incidentally to support argumentation, in discussing related work, or be used in comparison with focus entities of direct interest. distinguishing between these focus and background entities might improve the selection of information most relevant to a user. the automatic identification of entities in text is typically achieved using named entity recognition or entity linking methods based on dictionary, rule-based and/or machine learning methods, and aims to identify all mentions of entities of the target type(s). however, not all entities correctly identified in a text may be entities relevant for further processing or important to the main conclusions of a document. for example, it has been suggested that only ~10% of chemical mentions play a major role within a chemical patent @xcite . strategies for identifying entities that are in focus in a document enable honing in on critical document information, and can support filtering out entities that are ancillary to the main objectives of the work, e.g. for literature-based discovery applications @xcite . in this work, we introduce two large datasets annotated with focus and background entities that support experimentation with methods for distinguishing these two types of entity mentions foot_0 . we evaluated several machine learning algorithms on these dataset, setting baseline results for future work to be done on this task, and laying the foundation for more nuanced treatment of document entities in document retrieval or in summarisation. ## related work entity salience, relevant to identifying focus entities, has been discussed in previous work. use of discourse structure has been suggested in previous work on entity salience @xcite . the work of @xcite evaluates a comprehensive set of features, showing that the discourse structure and centrality may support predicting entity salience. one hypothesis is that the focus and background entities are distributed in specific argumentative sections of a document @xcite . the identification of focus entities has multiple relevant applications. in information retrieval (ir), the objective is to recover documents that are relevant to the user information needs, which is challenging for long documents @xcite as a larger number of entities are being mentioned. in information extraction (ie), we find the task of named entity recognition (ner), in which the objective is to identify entities of interest, from people and locations to proteins and genes, depending on the domain. in ner, all entities of a certain type are identified, even the ones that are not the main focus @xcite . our study relates specifically to identification of biological pathogen entities in scientific literature. pathogen ner has been studied in the bacteria biotope shared task @xcite . the geoboost tool @xcite addresses the identification of entities from the gene database genbank @xcite and largely includes information about viruses and bacteria. ## datasets development of large corpora is costly since human annotation is slow and expensive. there are biomedical datasets that have been manually annotated and could be considered as proxy for manual annotation. for this work, we have developed two large corpora automatically using existing resources from the national center for biotechnology information (ncbi) at the nlm. the corpora are targeted to microbial pathogens, some of the most relevant entities for infectious diseases @xcite , such as covid-19. ## results ## discussion the datasets we have constructed for the identification of focus entities are large, supporting eval- the distribution of entities in article sections ( ## conclusions and future work we have developed two large datasets of scientific documents for the study of the identification of focus entities. we find that short documents, represented by medline citations, are easier to process than longer (full-text) documents. transformer methods showed higher performance. future work will address using the proposed methods in scenarios in which focus entities become relevant, and comparing our approach with other existing methods @xcite .