Automatic Chinese Tense Tagging and Its Application to Causal Effect Detection

Causal analysis is an attractive topic in natural language processing and can be aplied in a variety of tasks such as event extaction, causality inference, and question-answering. This thesis explores the role of tense information in Chinese causal analysis. A semi-supervised approach is proposed for Chinese tense labelling. Both tasks of causal type classification and causal directionality identififcation are experimented to show the significant improvement gained from tense features. Unlike English, which has grammatical tense information, it is more challenging to predict the tense of a Chinese verb. Based on English-Chinese parallel data from UM-Corpus, we propose an approach that automatically aligns the tense information from English sentences to their Chinese counterparts. The large amount of pseudo-labelled Chinese tense instances are used to train the Chinese tense predictor. Our semi-supervised approach improves the dependency-based convolutional neural network (DCNN) models for Chinese tense labelling. Finally, the Chinese tense information is used as features for the tasks of casual type classification and causal directionality identification. Experimental results show the tense features significantly improve the performances of both tasks.