Chinese Explicit and Implicit Discourse Analysis

　In recent years, research in natural language processing, with the study words, phrases levels become more sophisticated. Since the large-scale manually annotated corpus of discourse relations such as PDTB and RST-DT have been released, the study of discourse relation is increasing. If we could correctly predict the relationship between discourse, it will help to understand the semantic understanding. The related applications in natural language processing, such as QA systems, automatic summaries are also of great help.
　However, due to the lack of a corpus of Chinese resources, the study in Chinese discourse relations are still little currently.
　In this work, we first make a preliminary analysis for HIT-CIR Chinese Discourse Relations Corpus, Harbin Institute of Technology released in 2013. Because of small-scale of datasets, we turn to treat another large-scale pseudo dataset as the training set. Experimental results show that this large-scale corpus training model promote to predict the discourse relation of text from different sources.
　Finally, we were further analyzed to the classification performance of implicit and explicit discourse relations, and analyzed whether the non-primary Markers is relevance to its discourse relation.