Artificial Intelligence - Special volume on natural language processing
A problem for RST: the need for multi-level discourse analysis
Computational Linguistics
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
The rhetorical parsing, summarization, and generation of natural language texts
Applied morphological processing of English
Natural Language Engineering
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Multiple discourse marker occurrence: creating hierarchies for natural language generation
Proceedings of the workshop on Student research
A maximum entropy approach to identifying sentence boundaries
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
A decision-based approach to rhetorical parsing
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for 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
Anaphora and Discourse Structure
Computational Linguistics
Head-Driven Statistical Models for Natural Language Parsing
Computational Linguistics
Acquiring the meaning of discourse markers
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Generating discourse structures for written texts
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Discourse chunking and its application to sentence compression
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Sentential structure and discourse parsing
DiscAnnotation '04 Proceedings of the 2004 ACL Workshop on Discourse Annotation
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Cue phrase classification using machine learning
Journal of Artificial Intelligence Research
Probabilistic head-driven parsing for discourse structure
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Prosodic correlates of rhetorical relations
ACTS '09 Proceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech
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 sense prediction for implicit discourse relations in text
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
Reliable discourse markers for contrast relations
IWCS-8 '09 Proceedings of the Eighth International Conference on Computational Semantics
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Using entity features to classify implicit discourse relations
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
The effects of discourse connectives prediction on implicit discourse relation recognition
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Discourse indicators for content selection in summarization
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Using syntactic and semantic based relations for dialogue act recognition
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Realization of discourse relations by other means: alternative lexicalizations
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Predicting discourse connectives for implicit discourse relation recognition
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Unlocking medical ontologies for non-ontology experts
BioNLP '11 Proceedings of BioNLP 2011 Workshop
Modelling discourse relations for Arabic
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Approaches to text mining arguments from legal cases
Semantic Processing of Legal Texts
The use of granularity in rhetorical relation prediction
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Discourse structure and language technology
Natural Language Engineering
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Being able to identify which rhetorical relations (e.g., contrast or explanation) hold between spans of text is important for many natural language processing applications. Using machine learning to obtain a classifier which can distinguish between different relations typically depends on the availability of manually labelled training data, which is very time-consuming to create. However, rhetorical relations are sometimes lexically marked, i.e., signalled by discourse markers (e.g., because, but, consequently etc.), and it has been suggested (Marcu and Echihabi, 2002) that the presence of these cues in some examples can be exploited to label them automatically with the corresponding relation. The discourse markers are then removed and the automatically labelled data are used to train a classifier to determine relations even when no discourse marker is present (based on other linguistic cues such as word co-occurrences). In this paper, we investigate empirically how feasible this approach is. In particular, we test whether automatically labelled, lexically marked examples are really suitable training material for classifiers that are then applied to unmarked examples. Our results suggest that training on this type of data may not be such a good strategy, as models trained in this way do not seem to generalise very well to unmarked data. Furthermore, we found some evidence that this behaviour is largely independent of the classifiers used and seems to lie in the data itself (e.g., marked and unmarked examples may be too dissimilar linguistically and removing unambiguous markers in the automatic labelling process may lead to a meaning shift in the examples).