C4.5: programs for machine learning
C4.5: programs for machine learning
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Computers and Biomedical Research
Learning Information Extraction Rules for Semi-Structured and Free Text
Machine Learning - Special issue on natural language learning
Relational learning of pattern-match rules for information extraction
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Text databases & document management
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Information Retrieval
Hierarchical Wrapper Induction for Semistructured Information Sources
Autonomous Agents and Multi-Agent Systems
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A shallow parser based on closed-class words to capture relations in biomedical text
Journal of Biomedical Informatics
Improving accuracy in word class tagging through the combination of machine learning systems
Computational Linguistics
Toward general-purpose learning for information extraction
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
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
MedPost: a part-of-speech tagger for bioMedical text
Bioinformatics
Determining the semantic orientation of terms through gloss classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Determining the sentiment of opinions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Adaptive information extraction from text by rule induction and generalisation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Collective-agreement-based pruning of ensembles
Computational Statistics & Data Analysis
A metalearning approach to processing the scope of negation
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Computational Statistics & Data Analysis
Learning the scope of negation in biomedical texts
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Creating and evaluating a consensus for negated and speculative words in a Swedish clinical corpus
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
Negation detection in Swedish clinical text
Louhi '10 Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents
Characterizing Mammography Reports for Health Analytics
Journal of Medical Systems
Modality and negation: An introduction to the special issue
Computational Linguistics
Mal-ID: automatic malware detection using common segment analysis and meta-features
The Journal of Machine Learning Research
UWashington: negation resolution using machine learning methods
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
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Substantial medical data, such as discharge summaries and operative reports are stored in electronic textual form. Databases containing free-text clinical narratives reports often need to be retrieved to find relevant information for clinical and research purposes. The context of negation, a negative finding, is of special importance, since many of the most frequently described findings are such. When searching free-text narratives for patients with a certain medical condition, if negation is not taken into account, many of the documents retrieved will be irrelevant. Hence, negation is a major source of poor precision in medical information retrieval systems. Previous research has shown that negated findings may be difficult to identify if the words implying negations (negation signals) are more than a few words away from them. We present a new pattern learning method for automatic identification of negative context in clinical narratives reports. We compare the new algorithm to previous methods proposed for the same task, and show its advantages: accuracy improvement compared to other machine learning methods, and much faster than manual knowledge engineering techniques with matching accuracy. The new algorithm can be applied also to further context identification and information extraction tasks.