Abstract
Chinese is a language that does not have morphological tense markers that provide explicit grammaticalization of the temporal location of situations (events or states). However, in many NLP applications such as Machine Translation, Information Extraction and Question Answering, it is desirable to make the temporal location of the situations explicit. We describe a machine learning framework where different sources of information can be combined to predict the temporal location of situations in Chinese text. Our experiments show that this approach significantly outperforms the most frequent tense baseline. More importantly, the high training accuracy shows promise that this challenging problem is solvable to a level where it can be used in practical NLP applications with more training data, better modeling techniques and more informative and generalizable features.