The structured weighted violations perceptron algorithm

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Abstract

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.

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APA

Dror, R., & Reichart, R. (2016). The structured weighted violations perceptron algorithm. In EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 469–478). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d16-1045

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