TY - GEN
T1 - Supervised model learning with feature grouping based on a discrete constraint
AU - Suzuki, Jun
AU - Nagata, Masaaki
PY - 2013
Y1 - 2013
N2 - This paper proposes a framework of supervised model learning that realizes feature grouping to obtain lower complexity models. The main idea of our method is to integrate a discrete constraint into model learning with the help of the dual decomposition technique. Experiments on two well-studied NLP tasks, dependency parsing and NER, demonstrate that our method can provide state-of-The-art performance even if the degrees of freedom in trained models are surprisingly small, i.e., 8 or even 2. This significant benefit enables us to provide compact model representation, which is especially useful in actual use.
AB - This paper proposes a framework of supervised model learning that realizes feature grouping to obtain lower complexity models. The main idea of our method is to integrate a discrete constraint into model learning with the help of the dual decomposition technique. Experiments on two well-studied NLP tasks, dependency parsing and NER, demonstrate that our method can provide state-of-The-art performance even if the degrees of freedom in trained models are surprisingly small, i.e., 8 or even 2. This significant benefit enables us to provide compact model representation, which is especially useful in actual use.
UR - http://www.scopus.com/inward/record.url?scp=84907341300&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907341300&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84907341300
SN - 9781937284510
T3 - ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
SP - 18
EP - 23
BT - Short Papers
PB - Association for Computational Linguistics (ACL)
T2 - 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
Y2 - 4 August 2013 through 9 August 2013
ER -