Abstract
Information mining from textual data becomes a very challenging task when the structure of the text record is very loose without any rules. Doctors often use natural language in medical records. Therefore it contains many ambiguities due to non-standard abbreviations and synonyms. The medical environment itself is also very specific: the natural language used in textual description varies with the personality creating the record (there are many personalized approaches), however it is restricted by terminology (i.e. medical terms, medical standards, etc.). Moreover, the typical patient record is filled with typographical errors, duplicates, ambiguities, syntax errors and many nonstandard abbreviations.
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