Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Computational Linguistics
Anaphora and Discourse Structure
Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Multi-perspective question answering using the OpQA corpus
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Attribution and the (non-)alignment of syntactic and discourse arguments of connectives
CorpusAnno '05 Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky
Annotating attributions and private states
CorpusAnno '05 Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky
Annotation and data mining of the Penn Discourse TreeBank
DiscAnnotation '04 Proceedings of the 2004 ACL Workshop on Discourse Annotation
Exploiting subjectivity classification to improve information extraction
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Innovations for Requirement Analysis. From Stakeholders' Needs to Formal Designs
BioNLP '09 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Discourse level opinion relations: an annotation study
SIGdial '08 Proceedings of the 9th SIGdial Workshop 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
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Modality and negation: An introduction to the special issue
Computational Linguistics
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An emerging task in text understanding and generation is to categorize information as fact or opinion and to further attribute it to the appropriate source. Corpus annotation schemes aim to encode such distinctions for NLP applications concerned with such tasks, such as information extraction, question answering, summarization, and generation. We describe an annotation scheme for marking the attribution of abstract objects such as propositions, facts and eventualities associated with discourse relations and their arguments annotated in the Penn Discourse TreeBank. The scheme aims to capture the source and degrees of factuality of the abstract objects. Key aspects of the scheme are annotation of the text spans signalling the attribution, and annotation of features recording the source, type, scopal polarity, and determinacy of attribution.