Min Jiang 1 , Todd Sanger 1 , Xiong Liu 1 Show Affiliations »
Abstract
BACKGROUND: Named entity recognition (NER) is a key step in clinical natural language processing (NLP). Traditionally, rule-based systems leverage prior knowledge to define rules to identify named entities. Recently, deep learning-based NER systems have become more and more popular. Contextualized word embedding, as a new type of representation of the word, has been proposed to dynamically capture word sense using context information and has proven successful in many deep learning-based systems in either general domain or medical domain. However, there are very few studies that investigate the effects of combining multiple contextualized embeddings and prior knowledge on the clinical NER task. OBJECTIVE: This study aims to improve the performance of NER in clinical text by combining multiple contextual embeddings and prior knowledge. METHODS: In this study, we investigate the effects of combining multiple contextualized word embeddings with classic word embedding in deep neural networks to predict named entities in clinical text. We also investigate whether using a semantic lexicon could further improve the performance of the clinical NER system. RESULTS: By combining contextualized embeddings such as ELMo and Flair, our system achieves the F-1 score of 87.30% when only training based on a portion of the 2010 Informatics for Integrating Biology and the Bedside NER task dataset. After incorporating the medical lexicon into the word embedding, the F-1 score was further increased to 87.44%. Another finding was that our system still could achieve an F-1 score of 85.36% when the size of the training data was reduced to 40%. CONCLUSIONS: Combined contextualized embedding could be beneficial for the clinical NER task. Moreover, the semantic lexicon could be used to further improve the performance of the clinical NER system. ©Min Jiang, Todd Sanger, Xiong Liu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 13.11.2019.
BACKGROUND: Named entity recognition (NER) is a key step in clinical natural language processing (NLP). Traditionally, rule-based systems leverage prior knowledge to define rules to identify named entities. Recently, deep learning-based NER systems have become more and more popular. Contextualized word embedding, as a new type of representation of the word, has been proposed to dynamically capture word sense using context information and has proven successful in many deep learning-based systems in either general domain or medical domain. However, there are very few studies that investigate the effects of combining multiple contextualized embeddings and prior knowledge on the clinical NER task. OBJECTIVE: This study aims to improve the performance of NER in clinical text by combining multiple contextual embeddings and prior knowledge. METHODS: In this study, we investigate the effects of combining multiple contextualized word embeddings with classic word embedding in deep neural networks to predict named entities in clinical text. We also investigate whether using a semantic lexicon could further improve the performance of the clinical NER system. RESULTS: By combining contextualized embeddings such as ELMo and Flair, our system achieves the F-1 score of 87.30% when only training based on a portion of the 2010 Informatics for Integrating Biology and the Bedside NER task dataset. After incorporating the medical lexicon into the word embedding, the F-1 score was further increased to 87.44%. Another finding was that our system still could achieve an F-1 score of 85.36% when the size of the training data was reduced to 40%. CONCLUSIONS: Combined contextualized embedding could be beneficial for the clinical NER task. Moreover, the semantic lexicon could be used to further improve the performance of the clinical NER system. ©Min Jiang, Todd Sanger, Xiong Liu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 13.11.2019.
Entities: Chemical
Disease
Gene
Species
Keywords:
contextualized word embedding; deep learning; named entity recognition; natural language processing; prior knowledge; semantic embedding
Year: 2019
PMID： 31719024 DOI： 10.2196/14850
Source DB: PubMed Journal: JMIR Med Inform