loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Tsendsuren Munkhdalai ; Meijing Li ; Khuyagbaatar Batsuren and Keun Ho Ryu

Affiliation: Chungbuk National University, Korea, Republic of

Keyword(s): Feature Learning, Semi-Supervised Learning, Named Entity Recognition, Conditional Random Fields.

Related Ontology Subjects/Areas/Topics: Algorithms and Software Tools ; Bioinformatics ; Biomedical Engineering ; Data Mining and Machine Learning

Abstract: Named Entity Recognition (NER) is an essential prerequisite task before effective text mining can begin for biomedical text data. Exploiting unlabeled text data to leverage system performance has been an active and challenging research topic in text mining due to the recent growth in the amount of biomedical literature. In this study, we take a step towards a unified NER system in biomedical, chemical and medical domain. We evaluate word representation features automatically learnt by a large unlabeled corpus for disease NER. The word representation features include brown cluster labels and Word Vector Classes (WVC) built by applying k-means clustering to continuous valued word vectors of Neural Language Model (NLM). The experimental evaluation using Arizona Disease Corpus (AZDC) showed that these word representation features boost system performance significantly as a manually tuned domain dictionary does. BANNER-CHEMDNER, a chemical and biomedical NER system has been extended with a disease mention recognition model that achieves a 77.84% F-measure on AZDC when evaluating with 10-fold cross validation method. BANNER-CHEMDNER is freely available at: https://bitbucket.org/tsendeemts/banner-chemdner. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.117.107.93

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Munkhdalai, T.; Li, M.; Batsuren, K. and Ryu, K. (2015). Towards a Unified Named Entity Recognition System - Disease Mention Identification. In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2015) - BIOINFORMATICS; ISBN 978-989-758-070-3; ISSN 2184-4305, SciTePress, pages 251-255. DOI: 10.5220/0005287802510255

@conference{bioinformatics15,
author={Tsendsuren Munkhdalai. and Meijing Li. and Khuyagbaatar Batsuren. and Keun Ho Ryu.},
title={Towards a Unified Named Entity Recognition System - Disease Mention Identification},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2015) - BIOINFORMATICS},
year={2015},
pages={251-255},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005287802510255},
isbn={978-989-758-070-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOSTEC 2015) - BIOINFORMATICS
TI - Towards a Unified Named Entity Recognition System - Disease Mention Identification
SN - 978-989-758-070-3
IS - 2184-4305
AU - Munkhdalai, T.
AU - Li, M.
AU - Batsuren, K.
AU - Ryu, K.
PY - 2015
SP - 251
EP - 255
DO - 10.5220/0005287802510255
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic