Hello all, I am a trained mathematician/computer scientist/programmer jumping into NLP and excited by the challenge but intimidated by the algorithm and software options. Specifically, I am at University of Texas and am charged with putting to good use our large database of (more-or-less unused) clinical notes. My strategy is roughly:
1. Learn the theory of NLP and Information Extraction. 2. Understand the publicly available software packages so as to avoid reinventing the wheel. 3. Apply #2 to our database and begin experimenting. My question in this post centers on #2. Not being a software engineer (though having lots of scientific programming experience), I am sometimes puzzled by "frameworks" and "components". I think of everything as libraries of functions. Yes, I know this view is outdated. I can wrap my head around NLP packages like Lingpipe and NLTK but am unclear what a package like UIMA offers over and above these types of pure libraries. Given what I've told you about my background (scientist, programmer, but NOT software engineer) can someone explain to me how investing the time to learn UIMA will pay off in the long run? I've started to dig into the UIMA api but thought I'd throw this rather basic question out there, hoping someone wouldn't think it too naive for this forum. Thanks in advance! Mark Ettinger
