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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Training Global Linear Models for Chinese Word Segmentation
verfasst von : Dong Song, Anoop Sarkar
Erschienen in: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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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.