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This paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. A beam-search algorithm is used during both training and decoding phases of the method. The perceptron approach was implemented with the same feature set as that of an existing generative model (Roark, 2001a), and experimental results show that it gives competitive performance to the generative model on parsing the Penn treebank. We demonstrate that training a perceptron model to combine with the generative model during search provides a 2.1 percent F-measure improvement over the generative model alone, to 88.8 percent. . | Incremental Parsing with the Perceptron Algorithm Michael Collins MIT CSAIL mcollins@csail.mit.edu Brian Roark AT T Labs - Research roark@research.att.com Abstract This paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. A beam-search algorithm is used during both training and decoding phases of the method. The perceptron approach was implemented with the same feature set as that of an existing generative model Roark 2001a and experimental results show that it gives competitive performance to the generative model on parsing the Penn treebank. We demonstrate that training a perceptron model to combine with the generative model during search provides a 2.1 percent F-measure improvement over the generative model alone to 88.8 percent. 1 Introduction In statistical approaches to NLP problems such as tagging or parsing it seems clear that the representation used as input to a learning algorithm is central to the accuracy of an approach. In an ideal world the designer of a parser or tagger would be free to choose any features which might be useful in discriminating good from bad structures without concerns about how the features interact with the problems of training parameter estimation or decoding search for the most plausible candidate under the model . To this end a number of recently proposed methods allow a model to incorporate arbitrary global features of candidate analyses or parses. Examples of such techniques are Markov Random Fields Rat-naparkhi et al. 1994 Abney 1997 Della Pietra et al. 1997 Johnson et al. 1999 and boosting or perceptron approaches to reranking Freund et al. 1998 Collins 2000 Collins and Duffy 2002 . A drawback of these approaches is that in the general case they can require exhaustive enumeration of the set of candidates for each input sentence in both the training and decoding phases1. For example Johnson et al. 1999 and Riezler et al. 2002 use all parses generated by .
