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

ACL ID P12-1027
Title Fast Online Training with Frequency-Adaptive Learning Rates for Chinese Word Segmentation and New Word Detection
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

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 high...