Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Towards linking patients and clinical information: detecting UMLS concepts in e-mail
Journal of Biomedical Informatics - Special issue: Building nursing knowledge through infomatics: from concept representation to data mining
A shared task involving multi-label classification of clinical free text
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Reranking for biomedical named-entity recognition
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
MaxMatcher: biological concept extraction using approximate dictionary lookup
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Recognizing named entities in tweets
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Artificial Intelligence in Medicine
SympGraph: a framework for mining clinical notes through symptom relation graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
TwiNER: named entity recognition in targeted twitter stream
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Joint inference of named entity recognition and normalization for tweets
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Two-stage NER for tweets with clustering
Information Processing and Management: an International Journal
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ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts
Journal of Biomedical Informatics
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This paper presents ongoing research in clinical information extraction. This work introduces a new genre of text which are not well-written, noise prone, ungrammatical and with much cryptic content. A corpus of clinical progress notes drawn form an Intensive Care Service has been manually annotated with more than 15000 clinical named entities in 11 entity types. This paper reports on the challenges involved in creating the annotation schema, and recognising and annotating clinical named entities. The information extraction task has initially used two approaches: a rule based system and a machine learning system using Conditional Random Fields (CRF). Different features are investigated to assess the interaction of feature sets and the supervised learning approaches to establish the combination best suited to this data set. The rule based and CRF systems achieved an F-score of 64.12% and 81.48% respectively.