How difficult is the adaptation of POS taggers?
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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How difficult is the adaptation of POS taggers? / Rodrigues, Irving Muller; Fernandes, Eraldo Rezende.
BRACIS 2017: 2017 Brazilian Conference on Intelligent Systems : Uberlândia, MG, Brazil, 2-5 October 2017 : proceedings. Piscataway : Institute of Electrical and Electronics Engineers Inc., 2017. p. 360-365.Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
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TY - CHAP
T1 - How difficult is the adaptation of POS taggers?
AU - Rodrigues, Irving Muller
AU - Fernandes, Eraldo Rezende
N1 - Conference code: 6
PY - 2017/6/28
Y1 - 2017/6/28
N2 - Domain adaptation is a difficult problem, but also a relevant one. Unsupervised domain adaptation focuses on adapting a model from a source domain, which includes plenty of labeled data, to a target domain that provides no labeled data. This is the most compelling setting of domain adaptation, but it is also the most difficult one. We perform an experimental analysis to highlight how difficult this problem is. We show that the best available unsupervised domain adaptation system for POS tagging can be outperformed by a simple POS tagger that has access to only 250 labeled sentences from the target domain. This is not a fair comparison between these two systems, of course; but it highlights that unsupervised domain adaptation is not well solved yet. Moreover, the best available systems are not yet practical, since they are complex, difficult to implement, and do not achieve significant improvements.
AB - Domain adaptation is a difficult problem, but also a relevant one. Unsupervised domain adaptation focuses on adapting a model from a source domain, which includes plenty of labeled data, to a target domain that provides no labeled data. This is the most compelling setting of domain adaptation, but it is also the most difficult one. We perform an experimental analysis to highlight how difficult this problem is. We show that the best available unsupervised domain adaptation system for POS tagging can be outperformed by a simple POS tagger that has access to only 250 labeled sentences from the target domain. This is not a fair comparison between these two systems, of course; but it highlights that unsupervised domain adaptation is not well solved yet. Moreover, the best available systems are not yet practical, since they are complex, difficult to implement, and do not achieve significant improvements.
KW - Informatics
KW - tagging
KW - training
KW - Feature extraction
KW - adaptation models
KW - natural language processing
KW - syntactics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85049536443&partnerID=8YFLogxK
U2 - 10.1109/BRACIS.2017.77
DO - 10.1109/BRACIS.2017.77
M3 - Article in conference proceedings
AN - SCOPUS:85049536443
SN - 978-1-5386-2408-1
SP - 360
EP - 365
BT - BRACIS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Piscataway
T2 - Brazilian Conference on Intelligent Systems - BRACIS 2017
Y2 - 2 October 2017 through 5 October 2017
ER -