Is Word Segmentation Necessary for Deep Learning of Chinese Representations?
Table 7: Results on the validation and the test set for text classification.
['[BOLD] Dataset', '[BOLD] description', '[BOLD] char valid', '[BOLD] word valid', '[BOLD] char test', '[BOLD] word test']
For text classification, we use the currently widely used benchmarks including: [topsep=0pt, partopsep=0pt] ChinaNews: Chinese news articles split into 7 news categories. Ifeng: First paragraphs of Chinese news articles from 2006-2016. The dataset consists of 5 news categories; JD_Full: product reviews in Chinese crawled from JD.com. The reviews are used to predict customers’ ratings (1 to 5 stars), making the task a five-class classification problem. JD_binary: the same product reviews from JD.com. We label 1, 2-star reviews as “negative reviews” and 4 and 5-star reviews as “positive reviews” (3-star reviews are ignored), making the task a binary-classification problem. Dianping: Chinese restaurant reviews crawled from the online review website Dazhong Dianping (similar to Yelp). We collapse the 1, 2 and 3-star reviews to “negative reviews” and 4 and 5-star reviews to “positive reviews”. The datasets were first introduced in \newcitezhang2017encoding. We trained the word-based version and the char-based version of bi-directional LSTM models to solve this task. As can be seen, the only dataset that the char-based model underperforms the word-based model is the chinanews dataset, but the difference is quite small (0.05). On all the other datasets, the char-based model significantly outperforms the word-based model.