Specialized models and ranking for coreference resolution
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Using syntax to disambiguate explicit discourse connectives in text
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Genre distinctions for discourse in the Penn TreeBank
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Automatic identification of cause-effect relations in tamil using CRFs
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Automatically evaluating text coherence using discourse relations
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Automatic question generation using discourse cues
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
Multilingual annotation and disambiguation of discourse connectives for machine translation
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Modelling discourse relations for Arabic
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Using sense-labeled discourse connectives for statistical machine translation
EACL 2012 Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra)
Discourse structure and computation: past, present and future
ACL '12 Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries
Discourse structure and language technology
Natural Language Engineering
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
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Automatically identifying the arguments of discourse connectives (e.g., and, because, however) is an important part of modeling discourse structure. Previous work used a single, general classifier for different connectives; however, connectives differ in their distribution and behavior, so conflating them this way loses discriminative power. Here, we show that using models for specific connectives and types of connectives and interpolating them with a general model improves performance. We also describe additional features that provide greater sensitivity to morphological, syntactic, and discourse patterns, and less sensitivity to parse quality. Our best model achieves a 3.6% absolute improvement over the state-of-the-art on identifying both arguments of discourse connectives when using features from gold-standard parses, and a 9.0% improvement when using automatically produced parses.