Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text (2021)

Abstract: Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities. Existing approaches are limited by the presence of coarse-grained structural resources in biomedical knowledge bases as well as the use of training datasets that provide low coverage over uncommon resources. In this work, we address these issues by proposing a cross-domain data integration method that transfers structural knowledge from a general text knowledge base to the medical domain. We utilize our integration scheme to augment structural resources and generate a large biomedical NED dataset for pretraining. Our pretrained model with injected structural knowledge achieves state-of-the-art performance on two benchmark medical NED datasets: MedMentions and BC5CDR. Furthermore, we improve disambiguation of rare entities by up to 57 accuracy points.

## introduction named entity disambiguation (ned), which involves mapping mentions in unstructured text to a structured knowledge base (kb), is a critical preprocessing step in biomedical text parsing pipelines @xcite . for instance, consider the following sentence: "we study snake evolution by focusing on a cis-acting enhancer of sonic hedgehog." in order to obtain a structured characterization of the sentence to be used in downstream applications, a ned system must map the mention sonic hedgehog to the entity shh gene. to do so, the system can use context cues such as "enhancer" and "evolution", which commonly refer to genes, to avoid selecting semantically similar concepts such as sonic hedgehog protein or sonic hedgehog signaling pathway. although ned systems have been successfully designed for general text corpora @xcite @xcite , the ned task remains particularly challenging in the medical setting due to the presence of rare entities that occur infrequently in medical literature @xcite . as a knowledge-intensive task, ned requires the incorporation of structural resources, such as entity descriptions and category types, to effectively disambiguate rare entities @xcite . however, this is difficult to accomplish in the medical setting for the following reasons: in this work, we design a biomedical ned system to improve disambiguation of rare entities through cross-domain data integration, which involves transferring knowledge between domains. data integration across heterogeneous domains is a challenging problem with potential applications across numerous knowledge-intensive tasks. here, we address this problem by utilizing a state-of-theart general text entity linker to map medical entities to corresponding items in wikidata, 2 a common general text kb. the key contributions of this work are listed below: 3 we evaluate our approach on two standard biomedical ned datasets: medmentions and bc5cdr. our results show that augmenting structural resources and pretraining across large datasets contribute to state-of-the-art model performance as well as up to a 57 point improvement in accuracy across rare entities that originally lack structural resources. to the best of our knowledge, this is the first study to address medical ned through structured knowledge integration. our cross-domain data integration approach can be translated beyond the medical domain to other knowledge-intensive tasks. recent state-of-the-art approaches for the medical 2 https://www.wikidata.org/wiki/ wikidata:main_page 3 code and data available at https://github.com/ hazyresearch/medical-ned-integration . ned task utilize transformer-based architectures to perform two tasks: candidate extraction, which involves identifying a small set of plausible entities, and reranking, which involves assigning likelihoods to each candidate. prior methods for this task generally limit the use of structural resources from medical kbs due to missing or limited information @xcite . as a result, several existing approaches have been shown to generalize poorly to rare entities @xcite . some previous studies have demonstrated that injecting auxiliary information, such as type or relation information, as well as pretraining can aid with model performance on various biomedical nlp tasks @xcite @xcite . however, these works are limited by the insufficient resources in medical kbs as well as the use of pretraining datasets that obtain low coverage over the entities in the kb. although some methods have been previously designed to enrich the metadata in medical ontologies with external knowledge, these approaches either use textmatching heuristics @xcite or only contain mappings for a small subset of medical entities @xcite . cross-domain structural knowledge integration has not been previously studied in the context of the medical ned task. ## methods we first present our cross-domain data integration approach for augmenting structural knowledge and obtaining pretraining datasets. we then describe the model architecture that we use to perform ned. ## evaluation we evaluate our model on two biomedical ned datasets and show that (1) our data integration approach results in state-of-the-art performance, (2) structural resource augmentation and pretraining are required in conjunction to realize improvements in overall accuracy, and (3) our approach contributes to a large performance lift on rare entities with limited structural resources. ## conclusion in this work, we show that cross-domain data integration helps achieve state-of-the-art performance on the named entity disambiguation task in medical text. the methods presented in this work help address limitations of medical knowledge bases and can be adapted for other knowledge-intensive problems.