Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text-Reference-Cited by-同舟云学术

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Integrating shortest dependency path and sentence sequence into a deep learning framework for relation extraction in clinical text

Published:2019-01 Issue:S1 Volume:19 Page:
ISSN:1472-6947
Container-title:BMC Medical Informatics and Decision Making
language:en
Short-container-title:BMC Med Inform Decis Mak

Author:

Li Zhiheng,Yang Zhihao,Shen Chen,Xu Jun,Zhang Yaoyun,Xu Hua

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Health Policy,Computer Science Applications

Link

http://link.springer.com/content/pdf/10.1186/s12911-019-0736-9.pdf

Reference37 articles.

1. Zelenko D, Aone C, Richardella A. Kernel methods for relation extraction. J Mach Learn Res. 2003;3:1083–106.

2. Rindflesch TC, Fiszman M. The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. J Biomed Inform. 2003;36:462–77.

3. Bunescu R, Mooney R, Ramani A, Marcotte E. Integrating co-occurrence statistics with information extraction for robust retrieval of protein interactions from Medline. Proceedings of the BioNLP Workshop on Linking Natural Language Processing and Biology at HLT-NAACL. 2006:49–56.

4. Song Q, Watanabe Y and Yokota H. Relationship extraction methods based on co-occurrence in web pages and files. Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services. ACM. 2011:82–89.

5. Leroy G, Chen H, Martinez JD. A shallow parser based on closed-class words to capture relations in biomedical text. J Biomed Inform. 2003;36:145–58.

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