Huang H.-H.Yang C.-R.Chen H.-H.2019-07-102019-07-1020169784879747020https://scholars.lib.ntu.edu.tw/handle/123456789/413099This paper explores the role of tense information in Chinese causal analysis. Both tasks of causal type classification and causal directionality identification are experimented to show the significant improvement gained from tense features. To automatically extract the tense features, a Chinese tense predictor is proposed. Based on large amount of parallel data, our semi-supervised approach improves the dependency-based convolutional neural network (DCNN) models for Chinese tense labelling and thus the causal analysis. ? 1963-2018 ACL.conference paper2-s2.0-85054996012https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054996012&partnerID=40&md5=ab57d7a84d120d8a6606b11c3a16d209