TY - GEN
T1 - Multi-domain learning
T2 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, EMNLP-CoNLL 2012
AU - Joshi, Mahesh
AU - Dredze, Mark
AU - Cohen, William W.
AU - Rose, Carolyn P.
PY - 2012
Y1 - 2012
N2 - We present a systematic analysis of existing multi-domain learning approaches with respect to two questions. First, many multi-domain learning algorithms resemble ensemble learning algorithms. (1) Are multi-domain learning improvements the result of ensemble learning effects? Second, these algorithms are traditionally evaluated in a balanced class label setting, although in practice many multi-domain settings have domain-specific class label biases. When multi-domain learning is applied to these settings, (2) are multi-domain methods improving because they capture domain-specific class biases? An understanding of these two issues presents a clearer idea about where the field has had success in multi-domain learning, and it suggests some important open questions for improving beyond the current state of the art.
AB - We present a systematic analysis of existing multi-domain learning approaches with respect to two questions. First, many multi-domain learning algorithms resemble ensemble learning algorithms. (1) Are multi-domain learning improvements the result of ensemble learning effects? Second, these algorithms are traditionally evaluated in a balanced class label setting, although in practice many multi-domain settings have domain-specific class label biases. When multi-domain learning is applied to these settings, (2) are multi-domain methods improving because they capture domain-specific class biases? An understanding of these two issues presents a clearer idea about where the field has had success in multi-domain learning, and it suggests some important open questions for improving beyond the current state of the art.
UR - http://www.scopus.com/inward/record.url?scp=84878113464&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84878113464&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84878113464
SN - 9781937284435
T3 - EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference
SP - 1302
EP - 1312
BT - EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference
Y2 - 12 July 2012 through 14 July 2012
ER -