An annotation scheme for discourse-level argumentation in research articles
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Semantic retrieval for the accurate identification of relational concepts in massive textbases
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Negation of protein–protein interactions
Bioinformatics
Multi-dimensional classification of biomedical text
Bioinformatics
Zone identification in biology articles as a basis for information extraction
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
A metalearning approach to processing the scope of negation
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Text type structure and logical document structure
DiscAnnotation '04 Proceedings of the 2004 ACL Workshop on Discourse Annotation
NAACL-Short '07 Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Modality and negation: An introduction to the special issue
Computational Linguistics
Are you sure that this happened? assessing the factuality degree of events in text
Computational Linguistics
Cross-genre and cross-domain detection of semantic uncertainty
Computational Linguistics
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The correct interpretation of biomedical texts by text mining systems requires the recognition of a range of types of high-level information (or meta-knowledge) about the text. Examples include expressions of negation and speculation, as well as pragmatic/rhetorical intent (e.g. whether the information expressed represents a hypothesis, generally accepted knowledge, new experimental knowledge, etc.) Although such types of information have previously been annotated at the text-span level (most commonly sentences), annotation at the level of the event is currently quite sparse. In this paper, we focus on the evaluation of the multi-dimensional annotation scheme that we have developed specifically for enriching bio-events with meta-knowledge information. Our annotation scheme is intended to be general enough to allow integration with different types of bio-event annotation, whilst being detailed enough to capture important subtleties in the nature of the meta-knowledge expressed in the text. To our knowledge, our scheme is unique within the field with regards to the diversity of meta-knowledge aspects annotated for each event, whilst the evaluation results have confirmed its feasibility and soundness.