TY  - CONF
ID  - SisLab1611
UR  - https://eprints.uet.vnu.edu.vn/eprints/id/eprint/1611/
A1  - Vu, Ngoc Trinh
A1  - Tran, Van Hien
A1  - Le, Hoang Quynh
A1  - Tran, Mai Vu
Y1  - 2015///
N2  - 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, class-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.
TI  - A Method for Building a Labeled Named Entity Recognition Corpus Using Ontologies
SP  - 141
M2  - Ho Chi Minh city, Vietnam
AV  - none
EP  - 149
T2  - KSE: the 2015 International Conference on Knowledge and Systems Engineering
ER  -