QQ客服 官方微博 反馈留言

	Fast online training with frequency-adaptive learning rates for chinese word segmentation and new word detection
	Sun, Xu ; Wang, Houfeng ; Li, Wenjie
	2012
英文摘要	We present a joint model for Chinese word segmentation and new word detection. We present high dimensional new features, including word-based features and enriched edge (label-transition) features, for the joint modeling. As we know, training a word segmentation system on large-scale datasets is already costly. In our case, adding high dimensional new features will further slow down the training speed. To solve this problem, we propose a new training method, adaptive online gradient descent based on feature frequency information, for very fast online training of the parameters, even given large-scale datasets with high dimensional features. Compared with existing training methods, our training method is an order magnitude faster in terms of training time, and can achieve equal or even higher accuracies. The proposed fast training method is a general purpose optimization method, and it is not limited in the specific task discussed in this paper. ? 2012 Association for computational Linguistics.; EI; 0
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/411931]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Sun, Xu,Wang, Houfeng,Li, Wenjie. Fast online training with frequency-adaptive learning rates for chinese word segmentation and new word detection. 2012-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。