Boundaries and edges rethinking: An end-to-end neural model for overlapping entity relation extraction

Abstract Background Extraction of adverse drug events from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around entity-relation extraction using bidirectional long short term memory networks (Bi-LSTM) which does not attain the best feature representations. Results In this paper, we introduce a question answering framework that exploits the robustness, masking and dynamic attention capabilities of RoBERTa by a technique of domain adaptation and attempt to overcome the aforementioned limitations. With formulation of an end-to-end pipeline, our model outperforms the prior work by 9.53% F1-Score. Conclusion An end-to-end pipeline that leverages state of the art transformer architecture in conjunction with QA approach can bolster the performances of entity-relation extraction tasks in the biomedical domain. In particular, we believe our research would be helpful in identification of potential adverse drug reactions in mono as well as combination therapy related textual data.

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A span-graph neural model for overlapping entity relation extraction in biomedical texts

Bioinformatics ◽

10.1093/bioinformatics/btaa993 ◽

2020 ◽

Author(s):

Hao Fei ◽

Yue Zhang ◽

Yafeng Ren ◽

Donghong Ji

Keyword(s):

Long Range ◽

Language Processing ◽

Relation Extraction ◽

Neural Model ◽

Joint Models ◽

Interaction Detection ◽

Depth Analysis ◽

Entity Relation Extraction ◽

Biomedical Texts ◽

Sequential Models

Abstract Motivation Entity relation extraction is one of the fundamental tasks in biomedical text mining, which is usually solved by the models from natural language processing. Compared with traditional pipeline methods, joint methods can avoid the error propagation from entity to relation, giving better performances. However, the existing joint models are built upon sequential scheme, and fail to detect overlapping entity and relation, which are ubiquitous in biomedical texts. The main reason is that sequential models have relatively weaker power in capturing long-range dependencies, which results in lower performance in encoding longer sentences. In this article, we propose a novel span-graph neural model for jointly extracting overlapping entity relation in biomedical texts. Our model treats the task as relation triplets prediction, and builds the entity-graph by enumerating possible candidate entity spans. The proposed model captures the relationship between the correlated entities via a span scorer and a relation scorer, respectively, and finally outputs all valid relational triplets. Results Experimental results on two biomedical entity relation extraction tasks, including drug–drug interaction detection and protein–protein interaction detection, show that the proposed method outperforms previous models by a substantial margin, demonstrating the effectiveness of span-graph-based method for overlapping relation extraction in biomedical texts. Further in-depth analysis proves that our model is more effective in capturing the long-range dependencies for relation extraction compared with the sequential models. Availability and implementation Related codes are made publicly available at http://github.com/Baxelyne/SpanBioER.

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