Re-moving PHI from these free text portions requires application of techniques from natural language processing that are capable of identifying phrases of specific types based on the lexical content ( the words that make up the phrases ) and the surround-ing words . 3 Current Methods and Metrics Fortunately , the problem of identifying types of information in free text is a well-studied problem in the natural language processing community . We can leverage several decades of research on infor-mation extraction and the named entity identifica-tion problem in particular , including multiple community evaluations such as the Message Un-derstanding Conferences ( MUC ) ( Grishman & Sundheim , 1996 ) and the subsequent Automated Content Extraction ( ACE ) evaluations1 ? both fo-cused on extraction from newswire -- as well as evaluations of biomedical entity extraction from the published literature e.g. , in the BioCreative evaluations ( Krallinger , et al , 2008 ) .