Abstract

This paper proposes a left-corner parser which can identify nonlocal dependencies. Our parser integrates nonlocal dependency identification into a transition-based system. We use a structured perceptron which enables our parser to utilize global features captured by nonlocal dependencies. An experimental result demonstrates that our parser achieves a good balance between constituent parsing and nonlocal dependency identification.

Highlights

  • Many constituent parsers based on the Penn Treebank (Marcus et al, 1993) are available, but most of them do not deal with nonlocal dependencies

  • This paper proposes a parser which integrates nonlocal dependency identification into constituent parsing

  • “CF” is the parser which was learned from the training data where nonlocal dependencies are removed. This result demonstrates that our nonlocal dependency identification does not have a bad influence on constituent parsing

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Summary

Introduction

Many constituent parsers based on the Penn Treebank (Marcus et al, 1993) are available, but most of them do not deal with nonlocal dependencies. Postprocessing approach performs constituent parsing and nonlocal dependency identification separately This means that the constituent parser cannot use any kind of information about nonlocal dependencies. Previous work on transition-based constituent parsing adopts a shift-reduce strategy with a tree binarization (Sagae and Lavie, 2005; Sagae and Lavie, 2006; Zhang and Clark, 2009; Zhu et al, 2013; Wang and Xue, 2014; Mi and Huang, 2015; Thang et al, 2015; Watanabe and Sumita, 2015), or convert constituent trees to “spinal trees”, which are similar to dependency trees (Ballesteros and Carreras, 2015) These conversions make it difficult for their parsers to capture c-command relations in the parsing process.

Nonlocal Dependency
Transition-Based Left-Corner Parsing
Nonlocal Dependency Identification
Empty Element Resolution
C-command Relation
Resolution Rules
Parsing Strategy
Features
Experiment
Conclusion
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