IGTree: Using Trees for Compression and Classification in Lazy LearningAlgorithms
Artificial Intelligence Review - Special issue on lazy learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Information Retrieval: A Health and Biomedical Perspective
Information Retrieval: A Health and Biomedical Perspective
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
A metalearning approach to processing the scope of negation
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Learning the scope of negation in biomedical texts
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Journal of Biomedical Informatics
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
Automatic extraction of lexico-syntactic patterns for detection of negation and speculation scopes
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Developing a robust part-of-speech tagger for biomedical text
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Inferring the scope of negation in biomedical documents
CICLing'12 Proceedings of the 13th international conference on Computational Linguistics and Intelligent Text Processing - Volume Part I
A machine-learning approach to negation and speculation detection in clinical texts
Journal of the American Society for Information Science and Technology
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More and more biomedical documents are digitally written and stored. To make the most of the rich resources, it is crucial to precisely locate the information pertinent to users' interests. One of the obstacles in finding information in natural language text is negations, which deny or reverse the meaning of a sentence or clause. This is especially problematic in the biomedical domain since scientific findings and clinical records often contain negated expressions to explicitly state negative effects or the absence of symptoms. Ignoring such negated expressions result in more irrelevant information and may even lead to false conclusions. Therefore, identifying negative words and their scopes are important sub-tasks in biomedical information processing. This paper reports on our ongoing work on a hybrid approach to negation identification combining statistical and heuristic approaches. Our approach is evaluated on three types of biomedical documents in comparison with an existing machine learning approach. In addition, the empirical results are manually analyzed to better understand the nature of the problems.