@inproceedings{e7492473dc36419e8b2c880ff1722455,
title = "Improving clinical named entity recognition with global neural attention",
abstract = "Clinical named entity recognition (NER) is a foundational technology to acquire the knowledge within the electronic medical records. Conventional clinical NER methods suffer from heavily feature engineering. Besides, these methods treat NER as a sentence-level task and ignore the long-range contextual dependencies. In this paper, we propose an attention-based neural network architecture to leverage document-level global information to alleviate the problem. The global information is obtained from document represented by pre-trained bidirectional language model (Bi-LM) with neural attention. The parameters of pre-trained Bi-LM which makes use of unlabeled data can be transferred to NER model to further improve the performance. We evaluate our model on 2010 i2b2/VA datasets to verify the effectiveness of leveraging global information and transfer strategy. Our model outperforms previous state-of-the-art method with less labeled data and no feature engineering.",
keywords = "Clinical named entity recognition, Language model, Neural attention",
author = "Guohai Xu and Chengyu Wang and Xiaofeng He",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018 ; Conference date: 23-07-2018 Through 25-07-2018",
year = "2018",
doi = "10.1007/978-3-319-96893-3\_20",
language = "英语",
isbn = "9783319968926",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "264--279",
editor = "Yi Cai and Yoshiharu Ishikawa and Jianliang Xu",
booktitle = "Web and Big Data - Second International Joint Conference, APWeb-WAIM 2018, Proceedings",
address = "德国",
}