2009 TrainingGlobalLinearModelsforCh

• (Song & Sarkar, 2009) ⇒ Dong Song, and Anoop Sarkar. (2009). “Training Global Linear Models for Chinese Word Segmentation.” In: Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence. doi:10.1007/978-3-642-01818-3_15

Subject Headings: Chinese Word Segmentation, Global Linear Models.

Cited By

• http://scholar.google.com/scholar?q=%22Training+Global+Linear+Models+for+Chinese+Word+Segmentation%22+2009
• http://dl.acm.org/citation.cfm?id=1560466.1560483&preflayout=flat#citedby

Quotes

Abstract

This paper examines how one can obtain state of the art Chinese word segmentation using global linear models. We provide experimental comparisons that give a detailed road-map for obtaining state of the art accuracy on various datasets. In particular, we compare the use of reranking with full beam search; we compare various methods for learning weights for features that are full sentence features, such as language model features; and, we compare an Averaged Perceptron global linear model with the Exponentiated Gradient max-margin algorithm.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 TrainingGlobalLinearModelsforCh	Anoop Sarkar Dong Song			Training Global Linear Models for Chinese Word Segmentation				10.1007/978-3-642-01818-3_15		2009