The Theory and Practice of Discourse Parsing and Summarization
The Theory and Practice of Discourse Parsing and Summarization
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Computing representations of the structure of written discourse
Computing representations of the structure of written discourse
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Towards constructive text, diagram, and layout generation for information presentation
Computational Linguistics
Sentence level discourse parsing using syntactic and lexical information
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Discriminative Reranking for Natural Language Parsing
Computational Linguistics
Boosting-based parse reranking with subtree features
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Discourse processing for context question answering based on linguistic knowledge
Knowledge-Based Systems
Generating Dialogues for Virtual Agents Using Nested Textual Coherence Relations
IVA '08 Proceedings of the 8th international conference on Intelligent Virtual Agents
Rich bitext projection features for parse reranking
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
A syntactic and lexical-based discourse segmenter
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
Discourse indicators for content selection in summarization
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
A sequential model for discourse segmentation
CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
Exploiting discourse information to identify paraphrases
Expert Systems with Applications: An International Journal
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This paper presents a discriminative reranking model for the discourse segmentation task, the first step in a discourse parsing system. Our model exploits subtree features to rerank N-best outputs of a base segmenter, which uses syntactic and lexical features in a CRF framework. Experimental results on the RST Discourse Treebank corpus show that our model outperforms existing discourse segmenters in both settings that use gold standard Penn Treebank parse trees and Stanford parse trees.