Automatic Labeling for Thai News Articles Based on Vector Representation of Documents

Nowadays, the most powerful news source in the world comes from online media on the Internet. The information comes from the SNS, video clips, audio clips or various news websites. In this competitive world, many news websites are mainly focused on publishing their contents to the website as fast as they can without taking time to label them correctly. This leads to a problem where readers cannot find news that they are interested in from a large amount of information on the website. In this paper, we propose a method to automatically label articles on the Thai language website using distributed representation of documents. The semantic similar words are extracted from paragraph vectors of each category of news and assign them as labels. We apply the convolutional neural network with binary classification approach to separate words from sentences and the result of the experiments indicated that our method can be applied to automatically label Thai news article effectively.