A Method for Building a Labeled Named Entity Recognition Corpus Using Ontologies

被引：0

作者：

Ngoc-Trinh Vu ^{[1
,2
]}

Van-Hien Tran ^{[1
]}

Thi-Huyen-Trang Doan ^{[1
]}

Hoang-Quynh Le ^{[1
]}

Mai-Vu Tran ^{[1
]}

机构：

[1] Vietnam Natl Univ Hanoi, Univ Engn & Technol, Knowledge Technol Lab, Hanoi, Vietnam

[2] Vietnam Natl Oil & Gas Grp, Vietnam Petr Inst, Hanoi, Vietnam

来源：

ADVANCED COMPUTATIONAL METHODS FOR KNOWLEDGE ENGINEERING | 2015年 / 358卷

关键词：

Named entity recognition; Phenotype; Machine learning; Biomedical ontology;

D O I：

10.1007/978-3-319-17996-4_13

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Building a labeled corpus which contains sufficient data and good coverage along with solving the problems of cost, effort and time is a popular research topic in natural language processing. The problem of constructing automatic or semi-automatic training data has become a matter of the research community. For this reason, we consider the problem of building a corpus in phenotype entity recognition problem, classs-specific feature detectors from unlabeled data based on over 10260 unique terms (more than 15000 synonyms) describing human phenotypic features in the Human Phenotype Ontology (HPO) and about 9000 unique terms (about 24000 synonyms) of mouse abnormal phenotype descriptions in the Mammalian Phenotype Ontology. This corpus evaluated on three corpora: Khordad corpus, Phenominer 2012 and Phenominer 2013 corpora with Maximum Entropy and Beam Search method. The performance is good for three corpora, with F-scores of 31.71% and 35.77% for Phenominer 2012 corpus and Phenominer 2013 corpus; 78.36% for Khordad corpus.

引用

页码：141 / 149

页数：9

共 11 条

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[2]

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