@InProceedings{	  niu4,
  author	= {Yun Niu and Xiaodan Zhu and Jianhua Li and Graeme Hirst},
  title		= {Analysis of polarity information in medical text},
  booktitle	= {Proceedings of the American Medical Informatics
		  Association 2005 Annual Symposium},
  address	= {Washington, D.C.},
  month		= {October},
  year		= {2005},
  pages		= {570--574},
  abstract	= {Knowing the polarity of clinical outcomes is important in
		  answering questions posed by clinicians in patient
		  treatment. We treat analysis of this information as a
		  classification problem. Natural language processing and
		  machine learning techniques are applied to detect four
		  possibilities in medical text: no outcome, positive
		  outcome, negative outcome, and neutral outcome. A
		  supervised learning method is used to perform the
		  classification at the sentence level. Five feature sets are
		  constructed: UNIGRAMS, BIGRAMS, CHANGE PHRASES, NEGATIONS,
		  and CATEGORIES. The performance of different combinations
		  of feature sets is compared. The results show that
		  generalization using the category information in the domain
		  knowledge base Unified Medical Language System is effective
		  in the task. The effect of context information is
		  significant. Combining linguistic features and domain
		  knowledge leads to the highest accuracy.},
  download	= {http://ftp.cs.toronto.edu/pub/gh/Niu-etal-2005.pdf}
}

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