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2012

Fast Online Training with Frequency-Adaptive Learning Rates for Chinese Word Segmentation and New Word Detection

9 years 3 months ago
Fast Online Training with Frequency-Adaptive Learning Rates for Chinese Word Segmentation and New Word Detection
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.
Xu Sun, Houfeng Wang, Wenjie Li
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where ACL
Authors Xu Sun, Houfeng Wang, Wenjie Li
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