TY - GEN
T1 - The structured weighted violations perceptron algorithm
AU - Dror, Rotem
AU - Reichart, Roi
N1 - Publisher Copyright: © 2016 Association for Computational Linguistics
PY - 2016
Y1 - 2016
N2 - We present the Structured Weighted Violations Perceptron (SWVP) algorithm, a new structured prediction algorithm that generalizes the Collins Structured Perceptron (CSP, (Collins, 2002)). Unlike CSP, the update rule of SWVP explicitly exploits the internal structure of the predicted labels. We prove the convergence of SWVP for linearly separable training sets, provide mistake and generalization bounds, and show that in the general case these bounds are tighter than those of the CSP special case. In synthetic data experiments with data drawn from an HMM, various variants of SWVP substantially outperform its CSP special case. SWVP also provides encouraging initial dependency parsing results.
AB - We present the Structured Weighted Violations Perceptron (SWVP) algorithm, a new structured prediction algorithm that generalizes the Collins Structured Perceptron (CSP, (Collins, 2002)). Unlike CSP, the update rule of SWVP explicitly exploits the internal structure of the predicted labels. We prove the convergence of SWVP for linearly separable training sets, provide mistake and generalization bounds, and show that in the general case these bounds are tighter than those of the CSP special case. In synthetic data experiments with data drawn from an HMM, various variants of SWVP substantially outperform its CSP special case. SWVP also provides encouraging initial dependency parsing results.
UR - http://www.scopus.com/inward/record.url?scp=85072843695&partnerID=8YFLogxK
U2 - https://doi.org/10.18653/v1/d16-1045
DO - https://doi.org/10.18653/v1/d16-1045
M3 - Conference contribution
T3 - EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings
SP - 469
EP - 478
BT - EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings
PB - Association for Computational Linguistics (ACL)
T2 - 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP 2016
Y2 - 1 November 2016 through 5 November 2016
ER -