We propose a novel approach for developing a two-stage document-level discourse parser. Our parser builds a discourse tree by applying an optimal parsing algorithm to probabilities inferred from two Conditional Random Fields: one for intrasentential parsing and the other for multisentential parsing. We present two approaches to combine these two stages of discourse parsing effectively. A set of empirical evaluations over two different datasets demonstrates that our discourse parser significantly outperforms the stateof-the-art, often by a wide margin.

@inproceedings{joty-carenini-ng-mehdad-acl-13, abstract = {We propose a novel approach for developing a two-stage document-level discourse parser. Our parser builds a discourse tree by applying an optimal parsing algorithm to probabilities inferred from two Conditional Random Fields: one for intrasentential parsing and the other for multisentential parsing. We present two approaches to combine these two stages of discourse parsing effectively. A set of empirical evaluations over two different datasets demonstrates that our discourse parser significantly outperforms the stateof-the-art, often by a wide margin.}, address = {Sofia, Bulgaria}, author = {Joty, Shafiq and Carenini, Giuseppe and Ng, Raymond T. and Mehdad, Yashar}, booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics}, numpages = {9}, pages = {486--496}, publisher = {ACL}, series = {ACL'13}, title = {{Combining Intra- and Multi-sentential Rhetorical Parsing for Document-level Discourse Analysis}}, year = {2013} }