ABSTRACT
Named entity annotation means an entity that needs to be labeled in a prediction sequence on a given text sequence. Labeling high-quality medical entities from Chinese medical texts plays an important role in named entity recognition, and construction of medical knowledge graph. Named entity annotation in medical texts is the premise of the full-supervised and semi-supervised named entities recognition. The current mainstream named entity annotation require a lot of manpower on the corpus labeling, which is laborious and time consuming. For medical entities widely distributed in Chinese medical texts, in this paper, we propose a small number of manually labeled medical entities to autonomously learn medical text features, and iteratively generating new labeled entities. The model automatically iterates the annotations from the original medical text collection to be processed and generates a valid medical entity. The autonomously medical entity labeling work makes it easy to label Chinese medical texts. This framework is tested on real Chinese medical records, and the experimental results show that the method can effectively identify the entities, and has certain practical value.
- Francisco S. Roque, Peter Bjødstrup Jensen, Henriette Schmock, Marlene Dalgaard, Massimo Andreatta, Thomas F. Hansen, Karen Søeby, Søren Bredkjær, Anders Juul, Thomas Werge, Lars Juhl Jensen, Søren Brunak: Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts. PLoS Computational Biology 7(8) (2011)Google Scholar
- Noha Alnazzawi, Paul Thompson, Riza Theresa Batista-Navarro, Sophia Ananiadou: Using text mining techniques to extract phenotypic information from the PhenoCHF corpus. BMC Med. Inf. & Decision Making 15(S-2): S3 (2015)Google Scholar
- Meystre, Stéphane M., Guergana K. Savova, Karin C. Kipper-Schuler, and John F. Hurdle. "Extracting information from textual documents in the electronic health record: a review of recent research." Yearbook of medical informatics 17, no. 01 (2008): 128--144.Google ScholarCross Ref
- Yonghui Wu, Min Jiang, Jianbo Lei, Hua Xu: Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network. MedInfo 2015: 624--628Google Scholar
- Pinal Patel, Disha Davey, Vishal Panchal, Parth Pathak: Annotation of a Large Clinical Entity Corpus. EMNLP 2018: 2033--2042Google Scholar
- Martin Jansche, Steven Abney: Information Extraction from Voicemail Transcripts. EMNLP 2002Google Scholar
- Jing Huang, Geoffrey Zweig, Mukund Padmanabhan: Information Extraction from Voicemail. ACL 2001: 290--297Google Scholar
- Daniel M. Bikel, Scott Miller, Richard M. Schwartz, Ralph M. Weischedel: Nymble: a High-Performance Learning Name-finder. ANLP 1997: 194--201Google Scholar
- Tzong-Han Tsai, Shih-Hung Wu, Cheng-Wei Lee, Cheng-Wei Shih, Wen-Lian Hsu: Mencius: A Chinese Named Entity Recognizer Using the Maximum Entropy-based Hybrid Model. IJCLCLP 9(1) (2004)Google Scholar
- Wei Li, Andrew McCallum: Rapid development of Hindi named entity recognition using conditional random fields and feature induction. ACM Trans. Asian Lang. Inf. Process. 2(3): 290--294 (2003)Google ScholarDigital Library
- Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder: Joint entity recognition and relation extraction as a multi-head selection problem. Expert Syst. Appl. 114: 34--45 (2018)Google ScholarCross Ref
- Sameer Pradhan, Noémie Elhadad, Brett R. South, David Martínez, Lee M. Christensen, Amy Vogel, Hanna Suominen, Wendy W. Chapman, Guergana Savova: Evaluating the state of the art in disorder recognition and normalization of the clinical narrative. JAMIA 22(1): 143--154 (2015)Google Scholar
- Aleksandar Kovacevic, Azad Dehghan, Michele Filannino, John A. Keane, Goran Nenadic: Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives. JAMIA 20(5): 859--866 (2013)Google Scholar
- Aditi Chaudhary, Jiateng Xie, Zaid Sheikh, Graham Neubig, Jaime G. Carbonell: A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers. CoRR abs/1908.08983 (2019)Google Scholar
- Powers, David Martin. "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation." (2011).Google Scholar
Index Terms
- Chinese Medical Entity Annotation Based on Autonomous Learning
Recommendations
Named Entity Extraction for Chinese Electronic Medical Records
CSAI '19: Proceedings of the 2019 3rd International Conference on Computer Science and Artificial IntelligenceNamed entity extraction task refers to identifying and extracting proper named entities from natural language texts. It is the key task in knowledge graph construction. Disease, symptom and drug entities are widely distributed in Chinese electronic ...
A BiLSTM-CRF Method to Chinese Electronic Medical Record Named Entity Recognition
ACAI '18: Proceedings of the 2018 International Conference on Algorithms, Computing and Artificial IntelligenceWith the application of electronic medical records in medical field, more and more people are paying attention to how to use these data efficiently. In this paper, the BiLSTM-CRF model is applied to Chinese electronic medical records to recognize ...
Medical Knowledge Attention Enhanced Neural Model for Named Entity Recognition in Chinese EMR
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big DataAbstractNamed entity recognition (NER) in Chinese electronic medical records (EMRs) has become an important task of clinical natural language processing (NLP). However, limited studies have been performed on the clinical NER study in Chinese EMRs. ...
Comments