Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
DiMLex: a lexicon of discourse markers for text generation and understanding
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Coarse-to-fine n-best parsing and MaxEnt discriminative reranking
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Discourse Connective Argument Identification with Connective Specific Rankers
ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
SigDIAL '06 Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Using syntax to disambiguate explicit discourse connectives in text
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
A novel discourse parser based on support vector machine classification
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
Towards a multidimensional semantics of discourse markers in spoken dialogue
IWCS-8 '09 Proceedings of the Eighth International Conference on Computational Semantics
Disambiguating temporal-contrastive discourse connectives for machine translation
HLT-SS '11 Proceedings of the ACL 2011 Student Session
BUCC '11 Proceedings of the 4th Workshop on Building and Using Comparable Corpora: Comparable Corpora and the Web
Hi-index | 0.00 |
Many discourse connectives can signal several types of relations between sentences. Their automatic disambiguation, i.e. the labeling of the correct sense of each occurrence, is important for discourse parsing, but could also be helpful to machine translation. We describe new approaches for improving the accuracy of manual annotation of three discourse connectives (two English, one French) by using parallel corpora. An appropriate set of labels for each connective can be found using information from their translations. Our results for automatic disambiguation are state-of-the-art, at up to 85% accuracy using surface features. Using feature analysis, contextual features are shown to be useful across languages and connectives.