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Sentence Compression with Reinforcement Learning Read chapter Read first chapter

2018 | OriginalPaper | Chapter

A Biomedical Question Answering System Based on SNOMED-CT

Authors : Xinhua Zhu, Xuechen Yang, Hongchao Chen

Published in: Knowledge Science, Engineering and Management

Publisher: Springer International Publishing

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Abstract

Biomedical question answering system is an important research topic in biomedical natural language processing. To make full use of the semantic knowledge in SNOMED-CT for clinical medical service, we developed a biomedical question answering system based on SNOMED-CT, which has the following characteristics: (a) this system takes the semantic network in SNOMED-CT as a knowledge base to answer the clinical questions posed by physicians in natural language form, (b) a multi-layer nested structure of question templates is designed to map a template into the different semantic relationships in SNOMED-CT, (c) a template description logic system is designed to define the question templates and tag template elements so as to accurately represent question semantics, and (d) a textual entailment algorithm with semantics is proposed to match the question templates in order to consider both the flexibility and accuracy of the system. The experimental results show that the overall performance of the system has reached a high level, which can give 85% of the correct answer and be used as a biomedical question answering system in a real environment.

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Metadata
Title
A Biomedical Question Answering System Based on SNOMED-CT
Authors
Xinhua Zhu
Xuechen Yang
Hongchao Chen
Copyright Year
2018
Book
Knowledge Science, Engineering and Management

Print ISBN: 978-3-319-99364-5

Electronic ISBN: 978-3-319-99365-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-99365-2_2

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