A Common Case of Jekyll and Hyde: The Synergistic Effect of Using Divided Source Training Data for Feature Augmentation
Yan Song, Fei Xia · 2013
Feature augmentation is a well-known method for domain adaptation and has been shown to be effective when tested on several NLP tasks (Daume III, 2007). However, a limitation of the method is that it requires labeled data from the target do-main and very often such data is unavail-able. In this paper, we propose to use train-ing data selection to divide the source do-main training data into two parts, pseudo target data (the selected part) and source data (the unselected part), and then ap-ply feature augmentation on the two parts of the training data. This approach has two advantages: first, feature augmenta-tion can be applied even when there is no labeled data from the target domain; sec-ond, the approach can take advantage of all the training data including the part that is not selected by training data selection. We evaluate the approach on Chinese word segmentation and part-of-speech tagging and show that it outperforms the baseline where no feature augmentation is applied. 1