Abstract
BackgroundExtracting 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.ResultsOur 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.ConclusionsThe 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.
Highlights
Extracting biomedical entities and their relations from text has important applications on biomedical research
One likely reason is that their method did not distinguish between adverse drug event extraction (ADE) relations and drug-disease treatment relations due to the limitations of manually designed rules and knowledge bases, so this strategy led to a high recall but a low precision
Our model utilizes the advantages of several state-of-the-art neural models for entity recognition or relation classification in text mining and natural language processing (NLP)
Summary
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. We propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Extracting entities and their relations from biomedical text has attracted much research attention in biomedical text mining community due to its important applications on knowledge acquisition and ontology construction [1]. Taking the ADE task for example, the objective of this task is to recognize mentions of drug and disease entities, and extract possible ADE relations between them. Entity and relation extraction is a standard task in text mining or natural language processing (NLP). Pipeline models are frequently used for this task [8,9,10,11,12,13,14]
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