2011 AStackedSubWordModelforJointChi

• (Sun, 2011) ⇒ Weiwei Sun. (2011). “A Stacked Sub-word Model for Joint Chinese Word Segmentation and Part-of-speech Tagging.” In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1. ISBN:978-1-932432-87-9

Subject Headings: Chinese Word Segmentation.

Cited By

• http://scholar.google.com/scholar?q=%222011%22+A+Stacked+Sub-word+Model+for+Joint+Chinese+Word+Segmentation+and+Part-of-speech+Tagging
• http://dl.acm.org/citation.cfm?id=2002472.2002643&preflayout=flat#citedby

Quotes

Abstract

The large combined search space of joint word segmentation and Part-of-Speech (POS) tagging makes efficient decoding very hard. As a result, effective high order features representing rich contexts are inconvenient to use. In this work, we propose a novel stacked subword model for this task, concerning both efficiency and effectiveness. Our solution is a two step process. First, one word-based segmenter, one character-based segmenter and one local character classifier are trained to produce coarse segmentation and POS information. Second, the outputs of the three predictors are merged into sub-word sequences, which are further bracketed and labeled with POS tags by a fine-grained sub-word tagger. The coarse-to-fine search scheme is efficient, while in the sub-word tagging step rich contextual features can be approximately derived. Evaluation on the Penn Chinese Tree-bank shows that our model yields improvements over the best system reported in the literature.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 AStackedSubWordModelforJointChi	Weiwei Sun			A Stacked Sub-word Model for Joint Chinese Word Segmentation and Part-of-speech Tagging						2011