TY - GEN
T1 - EM can find pretty good HMM POS-Taggers (When given a good start)
AU - Goldberg, Yoav
AU - Adler, Meni
AU - Elhadad, Michael
PY - 2008/12/1
Y1 - 2008/12/1
N2 - We address the task of unsupervised POS tagging. We demonstrate that good results can be obtained using the robust EM-HMM learner when provided with good initial conditions, even with incomplete dictionaries. We present a family of algorithms to compute effective initial estimations p(t|w). We test the method on the task of full morphological disambiguation in Hebrew achieving an error reduction of 25% over a strong uniform distribution baseline. We also test the same method on the standard WSJ unsupervised POS tagging task and obtain results competitive with recent state-ofthe- art methods, while using simple and efficient learning methods.
AB - We address the task of unsupervised POS tagging. We demonstrate that good results can be obtained using the robust EM-HMM learner when provided with good initial conditions, even with incomplete dictionaries. We present a family of algorithms to compute effective initial estimations p(t|w). We test the method on the task of full morphological disambiguation in Hebrew achieving an error reduction of 25% over a strong uniform distribution baseline. We also test the same method on the standard WSJ unsupervised POS tagging task and obtain results competitive with recent state-ofthe- art methods, while using simple and efficient learning methods.
UR - https://www.scopus.com/pages/publications/84859881696
M3 - Conference contribution
AN - SCOPUS:84859881696
SN - 9781932432046
T3 - ACL-08: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference
SP - 746
EP - 754
BT - ACL-08
T2 - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-08: HLT
Y2 - 15 June 2008 through 20 June 2008
ER -