Bio- RFX : Refining Biomedical Extraction via Advanced Relation Classification and Structural Constraints (2024)

Abstract: The ever-growing biomedical publications magnify the challenge of extracting structured data from unstructured texts. This task involves two components: biomedical entity identification (Named Entity Recognition, NER) and their interrelation determination (Relation Extraction, RE). However, existing methods often neglect unique features of the biomedical literature, such as ambiguous entities, nested proper nouns, and overlapping relation triplets, and underutilize prior knowledge, leading to an intolerable performance decline in the biomedical domain, especially with limited annotated training data. In this paper, we propose the Biomedical Relation-First eXtraction (Bio-RFX) model by leveraging sentence-level relation classification before entity extraction to tackle entity ambiguity. Moreover, we exploit structural constraints between entities and relations to guide the model’s hypothesis space, enhancing extraction performance across different training scenarios. Comprehensive experimental results on biomedical datasets show that Bio-RFX achieves significant improvements on both NER and RE tasks. Even under the low-resource training scenarios, it outperforms all baselines in NER and has highly competitive performance compared to the state-of-the-art fine-tuned baselines in RE.

## introduction biomedical literature is a vital resource for research, but the surge in publications makes manual tracking of advances difficult. consequently, there's growing interest in methods for automatic extraction of structured information from these texts. this involves identifying biomedical entities and their relations from plain texts, namely named entity recognition (ner) and relation extraction (re), as illustrated in figure 1 . these structured data can be applied to several downstream tasks and real-world circumstances in academia and industry. the keystone of entity and relation extraction hinges on proficiently modeling textual data, which includes deriving meaningful biomedical text representations and developing methods to utilize them. the adaptation of bert @xcite architectures to the biomedical field, including pretraining and additional training, has seen significant success in recent years. however, two substantial challenges remain in this domain. firstly, learning effective representations is challenging in low-resource scenarios. neural networkbased strategies depend on substantial quantities of labeled training data, a prerequisite often elusive in the biomedical domain. this is mainly due to the labor-intensive, time-consuming, and error-prone nature of manually annotating biomedical text data. detailed reading and interpretation are required for annotation, and reliable annotations often necessitate domain experts or multiple annotation rounds. some studies focus on incorporating biomedical knowledge graphs (kgs) like umls @xcite into training data to improve cross-domain adaptability @xcite . nonetheless, this approach is subject to several limitations. biomedical kgs like umls, can be sizeable (27.1 gb), leading to large storage space and heavy computational costs. besides, while most biomedical information extraction tasks focus on extracting fine-grained relations between coarse-grained entities, kgs often struggle to differentiate between relation types. for instance, all relation types in drugprot @xcite and drugvar @xcite datasets are classified as the same type (interact-with) in umls. this classification significantly diminishes the instructive value of prior knowledge in kgs, especially when using triplets from kgs in extraction tasks @xcite . secondly, biomedical literature's unique features necessitate domain-specific model design, an area less explored than text representations. the performance of general-domain models drops dramatically when adapting to biomedical contexts due to the stylized writing and domain-specific terminology. moreover, biomedical entities can be ambiguous, with the same phrases recognized as different entities depending on context and relationships with other entities. for instance, in figure 1 example a, beta and delta could refer to various entities, but their binding relation suggests they're proteins. furthermore, overlapping proper nouns can perplex models, making entity detection challenging. in figure 1 example b, both human and human pathogens are valid entities, but only the latter should be extracted under the exhibit relation type. these factors make it hard for generaldomain models to effectively handle biomedical literature's distinctive features. to address these issues, we proposed biomedical relation-first extraction (bio-rfx) model, wherein hypothesis space is constrained by prior knowledge. this architecture, inspired by the strong structural knowledge implications among relational triplets, first predicts the relation types that appeared in the sentence. it then extracts relevant entities satisfying such structure through a question-answering approach. a question is generated based on the relation type, with the original sentence as context, and related entities form a multi-span answer. we then predict the sentence's valid entity count and remove false entities using the text-nms algorithm @xcite . finally, relations between entities are generated according to structural constraints. this approach is capable of tackling specific issues in biomedical texts. for ambiguous entities, the predicted relation information guides entity type identification. for perplexing entities, overlapping terms are eliminated by the text-nms algorithm, enhancing specificity. we evaluate our method on four biomedical datasets: drugprot @xcite , drug-var @xcite , bc5cdr @xcite and @xcite . experimental results show that our model achieves the best average rank among all the models. our model also surpasses the previous state-of-the-art, improving ner and re f1 scores by up to 2.91% and 1.86% respectively. the main contributions of this paper include: ## related work researchers have proposed a multitude of methods for extracting entities and relations, the majority of which fall under either pipeline or joint methods. ## method in this section, we detail the proposed bio-rfx, as illustrated in figure 2 . the framework contains four key components: (1) relation classifier predicts all the relation types that the input sentence expresses by performing a multi-label classification task. (2) entity span detector extracts subject and object entities for each relation in a sentence using a relation-specific question. (3) entity number predictor predicts the number of entities with a regression task in a question-answering manner. (4) pruning algorithm filters the candidate entities by the predicted entity number. ## experiments and analysis in this section, we validate our model's effectiveness through extensive sentence-level ner and re experiments. we begin with the experimental setup, followed by performance evaluation and analysis. we then explore our method's efficacy in a low-resource setting and conclude with an ablation study to highlight the impact of each submodule in our framework. ## conclusion this paper introduces bio-rfx, a novel biomedical entity and relation extraction method, using structural constraints for relation triplets to constrain the hypothesis space. the model tackles ambiguous entities and redundant relation prediction using a relation-first extraction approach, and uses a heuristic pruning algorithm for precise recognition of complex overlapping entity spans. experimental results on real-world biomedical datasets with abundant and limited training data show that bio-rfx outperforms the state-of-the-art methods in ner, and has highly competitive performance in re.