Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Assessing agreement on classification tasks: the kappa statistic
Computational Linguistics
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
A corpus-based investigation of definite description use
Computational Linguistics
Solving large scale linear prediction problems using stochastic gradient descent algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Journal of the American Society for Information Science and Technology
An empirical evaluation of knowledge sources and learning algorithms for word sense disambiguation
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Domain-specific language models and lexicons for tagging
Journal of Biomedical Informatics
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Applying alternating structure optimization to word sense disambiguation
CoNLL-X '06 Proceedings of the Tenth Conference on Computational Natural Language Learning
Rule-based information extraction from patients' clinical data
Journal of Biomedical Informatics
UMLS content views appropriate for NLP processing of the biomedical literature vs. clinical text
Journal of Biomedical Informatics
Disambiguation of ambiguous biomedical terms using examples generated from the UMLS Metathesaurus
Journal of Biomedical Informatics
Disambiguation in the biomedical domain: The role of ambiguity type
Journal of Biomedical Informatics
Resolving ambiguity in biomedical text to improve summarization
Information Processing and Management: an International Journal
Scaling up WSD with automatically generated examples
BioNLP '12 Proceedings of the 2012 Workshop on Biomedical Natural Language Processing
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The aim of this study is to explore the word sense disambiguation (WSD) problem across two biomedical domains-biomedical literature and clinical notes. A supervised machine learning technique was used for the WSD task. One of the challenges addressed is the creation of a suitable clinical corpus with manual sense annotations. This corpus in conjunction with the WSD set from the National Library of Medicine provided the basis for the evaluation of our method across multiple domains and for the comparison of our results to published ones. Noteworthy is that only 20% of the most relevant ambiguous terms within a domain overlap between the two domains, having more senses associated with them in the clinical space than in the biomedical literature space. Experimentation with 28 different feature sets rendered a system achieving an average F-score of 0.82 on the clinical data and 0.86 on the biomedical literature.