Using Discourse Structure to Differentiate Focus Entities from Background Entities in Scientific Literature (2021)

Abstract: In developing systems to identify focus entities in scientific literature, we face the problem of discriminating key entities of interest from other potentially relevant entities of the same type mentioned in the articles. We introduce the task of pathogen characterisation. We aim to discriminate mentions of biological pathogens, that are actively studied in the research presented in scientific publications. These are the pathogens that are the focus of direct experimentation in the research, rather than those that are referred to for context or as playing secondary roles. In this paper, we explore the hypothesis that these focus entities can be differentiated from other, non-actively studied, pathogens mentioned in articles through analysis of the patterns of mentions across different sections of a scientific paper, that is, using the discourse structure of the paper. We provide an indicative case study with the help of a small data set of PubMed abstracts that have been annotated with actively mentioned pathogens.

## introduction global monitoring of repositories of potentially harmful biological materials is an important component of ensuring the health and safety of our populations. in this context, we are building an information extraction system to identify information related to experimentation with potentially dangerous biological pathogens -e.g. viruses, bacteria, and biological toxins -as well as to detect facilities that may serve as repositories of harmful pathogens. this system will systematically scan open access data sets for evidence of research on those pathogens, thereby supporting gathering of information from public resources for biosecurity purposes @xcite . a key requirement for automated characterisation of research on pathogens using text-based in-formation sources, including the scientific literature, is to identify pathogens that are actively studied. an actively studied pathogen is defined as an organism that is subjected to direct physical experimentation in the research. recognition of potentially relevant entities is relatively advanced using biomedical named entity recognition tools that detect biological nomenclature such as the names of biological organisms (e.g. as studied in the context of biocreative @xcite ). however, differentiating mentions of actively studied organisms from other, background or incidental mentions of organisms poses a deeper natural language processing challenge. in the context of chemical patents, it has been suggested that only ~10% of chemical mentions play a major role within the patent @xcite . it is insufficient to simply detect a mention of a potentially relevant pathogen name; it must also be decided whether that pathogen is a focus of the experiments. the main goal of our pathogen characterisation task is therefore to enable filtering out pathogens that are mentioned in articles but not considered to be actively studied in the described research. publications may refer to pathogens in various ways. in addition to mentions in the context of direct experimentation, pathogens may be mentioned as part of background knowledge or in the context of discussion or comparison. we propose that a key element of identifying actively studied pathogens is understanding where in a publication a pathogen is described (e.g. in a methods segment vs. in the background segment of the paper), and how the pathogen is relevant to the research (e.g. mentions of the pathogen being subjected to specific tests or examinations that reveal experimentation). in this paper, we therefore explore the hypothesis that the context in a scientific paper where a potentially relevant entity is mentioned can provide clues about whether that entity is a focus (foregrounded) entity, or an entity in the background; our notion of an actively studied entity assumes that it is a focus of the research described in a paper. we investigate this hypothesis by comparing the distribution of focus and background entities across discourse segments, and apply association rule mining to identify combinations of segments that are relevant to identify focus entities. we present a small case study illustrating the proposed methodology, providing preliminary evidence of the value of discourse structure -consideration of where entities are mentioned -for identifying focus entities. ## related work identifying salient entities is a relevant component of information retrieval and text summarisation. the study 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. our task differs in that we adopt a narrower focus specifically on identification of actively studied pathogens in scientific research papers. pathogen characterisation has been studied in recent shared tasks, such as the bacteria biotope task @xcite . the tool geo-boost @xcite also addresses the identification of entities from genbank, which includes largely information about viruses and bacteria. the main role of geoboost is to identify the location of these biological entities, which requires performing natural language processing tasks in addition to combining information from ncbi resources. this work does not address saliency of entity mentions. in our work, we evaluate discourse features for direct identification of actively researched pathogens, covering a broad set of pathogen types. ## datasets in our experiments, we constructed a dataset based on information obtained from the biological material information program (bmip) foot_0 of the defense threat reduction agency (dtra) foot_1 . ## methods we approach identifying focus entities of scientific articles as a two-stage process: pathogen identification and pathogen characterisation. here, we describe our approach to each stage. ## results we ran the car m1 algorithm on our data set annotated with pathogen mentions and present the inferred rules in doing an analysis of the rules, we find that most of the rules indicate that a pathogen being mentioned in the title is a sign that it is a focus pathogen, which is expected since the title denotes the most important concepts of the article. the rules also indicate that a mention of a pathogen in the results segment is relevant to the classification of the entity as a focus pathogen. consideration of combinations of segments is more effective to identify focus entities than occurrence in any individual segment, apart from implication. ## conclusion we have proposed an approach to the problem of detecting focus versus ground entities using class association rules over entity mentions in discourse segments, specifically examining its use for pathogen characterisation. focus pathogens tend to appear in the title and results segments of abstracts, where the key findings of research are highlighted. our case study suggests that discourse information provides valuable cues to identify focus pathogens. given the small-scale data we have available, this work is only indicative of the promise of the approach. we are developing a larger data set, which will support comprehensive exploration of more refined rules. this data set would also support the exploration of additional existing methods, such as centrality and transformer based methods.