Annotating the Literature with Disease Ontology

YANG Li; ZHOU Yanhong; ZHENG Ying

doi:10.1049/cje.2017.09.020

Volume 26 Issue 6

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YANG Li, ZHOU Yanhong, ZHENG Ying, “Annotating the Literature with Disease Ontology,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1261-1268, 2017, doi: 10.1049/cje.2017.09.020

Citation:

YANG Li, ZHOU Yanhong, ZHENG Ying, “Annotating the Literature with Disease Ontology,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1261-1268, 2017, doi: 10.1049/cje.2017.09.020

YANG Li, ZHOU Yanhong, ZHENG Ying, “Annotating the Literature with Disease Ontology,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1261-1268, 2017, doi: 10.1049/cje.2017.09.020

Citation:

YANG Li, ZHOU Yanhong, ZHENG Ying, “Annotating the Literature with Disease Ontology,” Chinese Journal of Electronics, vol. 26, no. 6, pp. 1261-1268, 2017, doi: 10.1049/cje.2017.09.020

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Annotating the Literature with Disease Ontology

doi: 10.1049/cje.2017.09.020

1.
School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China;
2.
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61602060).

Received Date: 2014-12-01
Rev Recd Date: 2015-09-26
Publish Date: 2017-11-10

Abstract

Abstract

With the rapid growth of inquiry in biomedicine concerning diseases, the recognition of diseases becomes especially important. But only the recognition of the biomedical concepts in literature is not enough, annotations and normalizations of the concepts with normalized Metathesaurus get even more important. This paper proposes a system to annotate the literature with normalized Metathesaurus. First, a two-phase Conditional random fields (CRFs) is used to recognize the disease mentions, including the location and identification. Then, the paper adapts the Disease ontology (DO) to annotate the diseases recognized for normalization by computing the similarity between disease mentions and concepts. According to the similarities, the disease mentions are denoted as disease concepts and instances distinctively. The experiments carried out on the Arizona disease corpus show that our system makes a good achievement and outperforms the other works.
- Text mining,
- Disease ontology,
- Annotation,
- CRFs,
- Dictionary,
- Similarity

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References(14)

References

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