@InProceedings{boytcheva-nikolova-angelova:2017:RANLP,
  author    = {Boytcheva, Svetla  and  Nikolova, Ivelina  and  Angelova, Galia},
  title     = {Mining Association Rules from Clinical Narratives},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {130--138},
  abstract  = {Shallow text analysis (Text Mining) uses mainly Information Extraction
	techniques. The low resource languages do not allow application of such
	traditional techniques with sufficient accuracy and recall on big data. In
	contrast, Data Mining approaches provide an opportunity to make deep analysis
	and to discover new knowledge. Frequent pattern mining approaches are used
	mainly for structured information in databases and are a quite challenging task
	in text mining. Unfortunately, most frequent pattern mining approaches do not
	use contextual information for extracted patterns: general patterns are
	extracted regardless of the context. We propose a method that processes raw
	informal texts (from health discussion forums) and formal texts (outpatient
	records) in Bulgarian language. In addition we use some context information and
	small terminological lexicons to generalize extracted frequent patterns. This
	allows to map informal expression of medical terminology to the formal one and
	to generate automatically resources.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_019}
}