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Erschienen in:
Sentence Compression with Reinforcement Learning Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

A Biomedical Question Answering System Based on SNOMED-CT

verfasst von : Xinhua Zhu, Xuechen Yang, Hongchao Chen

Erschienen in: Knowledge Science, Engineering and Management

Verlag: 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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Vorheriges Kapitel Sentence Compression with Reinforcement Learning
Nächstes Kapitel Authorship Attribution for Short Texts with Author-Document Topic Model
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Metadaten
Titel
A Biomedical Question Answering System Based on SNOMED-CT
verfasst von
Xinhua Zhu
Xuechen Yang
Hongchao Chen
Copyright-Jahr
2018
Buch
Knowledge Science, Engineering and Management

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

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

Copyright-Jahr: 2018

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

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