Muyu Zhang, Vanessa Wei Feng, Bing Qin, Graeme Hirst, Ting Liu, Jingwen Huang. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

Text-level discourse parsing remains a challenge.The current state-of-the-art overall accuracy in relation assignment is 55.73%, achieved by Joty et al. (2013).However, their model has a high order of time complexity, and thus cannot be applied in practice.In this work, we develop a much faster model whose time complexity is linear in the number of sentences.Our model adopts a greedy bottom-up approach, with two linear-chain CRFs applied in cascade as local classifiers.To enhance the accuracy of the pipeline, we add additional constraints in the Viterbi decoding of the first CRF.In addition to efficiency, our parser also significantly outperforms the state of the art.Moreover, our novel approach of post-editing, which modifies a fully-built tree by considering information from constituents on upper levels, can further improve the accuracy.

In this paper we classify the temporal relations between pairs of events on an article-wide basis.This is in contrast to much of the existing literature which focuses on just event pairs which are found within the same or adjacent sentences.To achieve this, we leverage on discourse analysis as we believe that it provides more useful semantic information than typical lexico-syntactic features.We propose the use of several discourse analysis frameworks, including 1) Rhetorical Structure Theory (RST), 2) PDTB-styled discourse relations, and 3) topical text segmentation.We explain how features derived from these frameworks can be effectively used with support vector machines (SVM) paired with convolution kernels.Experiments show that our proposal is effective in improving on the state-of-the-art significantly by as much as 16% in terms of F 1 , even if we only adopt less-than-perfect automatic discourse analyzers and parsers.Making use of more accurate discourse analysis can further boost gains to 35%.