Distant Supervision for Chinese Temporal Tagging

Abstract

Temporal tagging plays an important role in many tasks such as event extraction and reasoning. Extracting Chinese temporal expressions is challenging because of the diversity of time phrases in Chinese. Usually researchers use rule-based methods or learning-based methods to extract temporal expressions. Rule-based methods can often achieve good results in certain types of text such as news but multi-type text with complex time phrases. Learning-based methods often require large amounts of annotated corpora which are hard to get, and the training data is difficult to extend to other tasks with different text type. In this paper, we consider time expression extraction as a sequence labeling problem and try to solve it by a popular model BiLSTM+CRF. We propose a distant supervision method using CN-DBPedia (an open domain Chinese knowledge graph) and BaiduBaike (one of the largest Chinese encyclopedias) to generate a dataset for model training. Results of our experiments on encyclopedia text and TempEval2 dataset indicate that the method is feasible. While obtaining acceptable tagging performance, our approach does not involve designing manual patterns as rule-based ones do, does not involve the constructing annotated data manually, and has a good adaptation to different types of text.