A neural joint model for entity and relation extraction from biomedical text

By Fei Li, Meishan Zhang, Guohong Fu, Donghong Ji. 2017

249 citations recorded by the source corpus.

Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.

Subfields: Artificial Intelligence, Molecular Biology

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Citations in the corpus

Joint entity recognition and relation extraction as a multi-head selection problem (2018, methodology): “Finally, we compare our model with previous work ( Li et al., 017; Li et al., 2016 ) on the ADE dataset. The previous models ( Li t al., 2017; Li et al., 2016 ) both use hand-crafted features or feaures derived from NLP tools. However, our model is able to outerform both models using the strict eval”
Span-based Joint Entity and Relation Extraction with Transformer Pre-training (2019, methodology): “Joint Entity and Relation Extraction Since entity detection and relation classiﬁcation may beneﬁt from exploiting interrelated signals, models for the joint detection of entities and relations have recently drawn attention (e.g., [3, 2, 21, 31, 40, 16]).”
RadGraph: Extracting Clinical Entities and Relations from Radiology Reports (2021, methodology): “Pipeline approaches [20, 21] decompose the task into separately trained subtasks (named entity recognition [22, 23] and relation extraction [24]), while joint extraction approaches [18, 25–27] model the two subtasks at the same time in order to capture interactions between entities and relations.”