Attention, intentions, and the structure of discourse
Computational Linguistics
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
The Theory and Practice of Discourse Parsing and Summarization
The Theory and Practice of Discourse Parsing and Summarization
TextTiling: segmenting text into multi-paragraph subtopic passages
Computational Linguistics
An annotation scheme for free word order languages
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A prosodic analysis of discourse segments in direction-giving monologues
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Discourse relations: a structural and presuppositional account using lexicalised TAG
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
Using Semantic Dependencies to Mine Depressive Symptoms from Consultation Records
IEEE Intelligent Systems
Recognizing implicit discourse relations in the Penn Discourse Treebank
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Evaluating hierarchical discourse segmentation
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Cross-argument inference for implicit discourse relation recognition
Proceedings of the 21st ACM international conference on Information and knowledge management
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We present a set of discourse structure relations that are easy to code, and develop criteria for an appropriate data structure for representing these relations. Discourse structure here refers to informational relations that hold between sentences in a discourse (cf. Hobbs, 1985). We evaluated whether trees are a descriptively adequate data structure for representing coherence. Trees are widely assumed as a data structure for representing coherence but we found that more powerful data structures are needed: In coherence structures of naturally occurring texts, we found many different kinds of crossed dependencies, as well as many nodes with multiple parents. The claims are supported by statistical results from a database of 135 texts from the Wall Street Journal and the AP Newswire that were hand-annotated with coherence relations, based on the annotation schema presented in this paper.