How To Obtain Powerful Parsers
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How To Obtain Powerful Parsers That Are Elegant and Practical
Most of the functional parsers published today focus on elegance. But they pay for this elegance by neglecting practical requirements such as efficiency and decent error handling. By contrast, a traditional parser (generator) based on LLor LR-principles focuses on efficiency and neglects issues such as elegance and understandability almost entirely. In this paper we combine these pragmatic issu...
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We develop a general dynamic programming technique for the tabulation of transition-based dependency parsers, and apply it to obtain novel, polynomial-time algorithms for parsing with the arc-standard and arc-eager models. We also show how to reverse our technique to obtain new transition-based dependency parsers from existing tabular methods. Additionally, we provide a detailed discussion of t...
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MaltOptimizer is a tool that is capable of finding an optimal configuration for MaltParser models, taking into account that nowadays dependency parsers require careful tuning in order to obtain state-of-the-art results, and this tuning is normally based on specialized knowledge. The Planar and 2-Planar parsers are two different parsing algorithms included in MaltParser. In the present paper, we...
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We introduce error-correcting parsing schemata, which allow us to define error-correcting parsers in a high-level, declarative way. This formalism can be used to describe error-correcting parsers in a simple and uniform manner, and provides a formal basis allowing to prove their correctness and other properties. We also show how these schemata can be used to obtain different implementations of ...
متن کاملSemi-supervised dependency parsing using generalized tri-training
Martins et al. (2008) presented what to the best of our knowledge still ranks as the best overall result on the CONLLX Shared Task datasets. The paper shows how triads of stacked dependency parsers described in Martins et al. (2008) can label unlabeled data for each other in a way similar to co-training and produce end parsers that are significantly better than any of the stacked input parsers....
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