Comparison of named entity recognition methodologies in biomedical documents.

Abstract:

BACKGROUND:Biomedical named entity recognition (Bio-NER) is a fundamental task in handling biomedical text terms, such as RNA, protein, cell type, cell line, and DNA. Bio-NER is one of the most elementary and core tasks in biomedical knowledge discovery from texts. The system described here is developed by using the BioNLP/NLPBA 2004 shared task. Experiments are conducted on a training and evaluation set provided by the task organizers. RESULTS:Our results show that, compared with a baseline having a 70.09% F1 score, the RNN Jordan- and Elman-type algorithms have F1 scores of approximately 60.53% and 58.80%, respectively. When we use CRF as a machine learning algorithm, CCA, GloVe, and Word2Vec have F1 scores of 72.73%, 72.74%, and 72.82%, respectively. CONCLUSIONS:By using the word embedding constructed through the unsupervised learning, the time and cost required to construct the learning data can be saved.

journal_name

Biomed Eng Online

authors

Song HJ,Jo BC,Park CY,Kim JD,Kim YS

doi

10.1186/s12938-018-0573-6

subject

Has Abstract

pub_date

2018-11-06 00:00:00

pages

158

issue

Suppl 2

issn

1475-925X

pii

10.1186/s12938-018-0573-6

journal_volume

17

pub_type

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