tailieunhanh - Báo cáo khoa học: "Advances in Discriminative Parsing"

The present work advances the accuracy and training speed of discriminative parsing. Our discriminative parsing method has no generative component, yet surpasses a generative baseline on constituent parsing, and does so with minimal linguistic cleverness. Our model can incorporate arbitrary features of the input and parse state, and performs feature selection incrementally over an exponential feature space during training. We demonstrate the flexibility of our approach by testing it with several parsing strategies and various feature sets. Our implementation is freely available at: . . | Advances in Discriminative Parsing Joseph Turian and I. Dan Melamed lastname @ Computer Science Department New York University New York New York 10003 Abstract The present work advances the accuracy and training speed of discriminative parsing. Our discriminative parsing method has no generative component yet surpasses a generative baseline on constituent parsing and does so with minimal linguistic cleverness. Our model can incorporate arbitrary features of the input and parse state and performs feature selection incrementally over an exponential feature space during training. We demonstrate the flexibility of our approach by testing it with several parsing strategies and various feature sets. Our implementation is freely available at http parser . 1 Introduction Discriminative machine learning methods have improved accuracy on many NLP tasks including POS-tagging shallow parsing relation extraction and machine translation. Some advances have also been made on full syntactic constituent parsing. Successful discriminative parsers have relied on generative models to reduce training time and raise accuracy above generative baselines Collins Roark 2004 Henderson 2004 Taskar et al. 2004 . However relying on information from a generative model might prevent these approaches from realizing the accuracy gains achieved by discriminative methods on other NLP tasks. Another problem is training speed Discriminative parsers are notoriously slow to train. In the present work we make progress towards overcoming these obstacles. We propose a flexible end-to-end discriminative method for training parsers demonstrating techniques that might also be useful for other structured prediction problems. The proposed method does model selection without ad-hoc smoothing or frequency-based feature cutoffs. It requires no heuristics or human effort to optimize the single important hyper-parameter. The training regime can use all available information from the entire .