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    <p><strong>Doing Things Manually</strong></p> <p>If the error messages are being generated automatically and the list of exceptions behind the messages is not terribly large, you might just want to have a table that directly maps each error message <em>type</em> to the people who need to be notified.</p> <p>This should make it easy to keep track of <strong>exactly</strong> who/which-groups will be getting what types of messages and to update the routing of messages should you decide that some of the messages are being misdirected. </p> <p>Typically, a small fraction of the types of errors make up a large fraction of error reports. For example, Microsoft noticed that <a href="http://www.crn.com/security/18821726;jsessionid=BOYX2LS0MUKSHQE1GHOSKH4ATMY32JVN" rel="nofollow noreferrer"><strong>80% of crashes were caused by 20% of the bugs</strong></a> in their software. So, to get something useful, you wouldn't even need to start with a complete table covering every type of error message. Instead, you could start with just a list that maps the most common errors to the right person and routes everything else to a person for manual routing. Each time an error is routed manually, you could then add an entry to the routing table so that errors of that type are handled automatically in the future.</p> <p><strong>Document Classification</strong></p> <p>Unless the error messages are being editorialized by people who submit them and you want to use this information when routing them, I wouldn't recommend treating this as a document classification task. However, if this is what you want to do, here's a list of reasonably good packages for document document classification organized by programming language:</p> <p><strong>Python</strong> - To do this using the Python based <strong><a href="http://www.nltk.org/" rel="nofollow noreferrer">Natural Language Toolkit (NLTK)</a></strong>, see the <a href="http://nltk.googlecode.com/svn/trunk/doc/book/ch06.html#document-classification" rel="nofollow noreferrer"><strong>Document Classification</strong></a> section in the freely available <a href="http://www.nltk.org/book" rel="nofollow noreferrer">NLTK book</a>.</p> <p><strong>Ruby</strong> - If Ruby is more of your thing, you can use the <strong><a href="http://classifier.rubyforge.org/" rel="nofollow noreferrer">Classifier</a></strong> gem. Here's sample code that detects <a href="http://www.igvita.com/2007/05/23/bayes-classification-in-ruby/" rel="nofollow noreferrer"> whether Family Guy quotes are funny or not-funny</a>.</p> <p><strong>C#</strong> - C# programmers can use <strong><a href="http://nbayes.codeplex.com/" rel="nofollow noreferrer">nBayes</a></strong>. The project's home page has sample code for a simple spam/not-spam classifier.</p> <p><strong>Java</strong> - Java folks have <strong><a href="http://classifier4j.sourceforge.net/" rel="nofollow noreferrer">Classifier4J</a></strong>, <a href="http://www.cs.waikato.ac.nz/ml/weka/" rel="nofollow noreferrer"><strong>Weka</strong></a>, <a href="http://lucene.apache.org/mahout/" rel="nofollow noreferrer"><strong>Lucene Mahout</strong></a>, and as adi92 mentioned <a href="http://mallet.cs.umass.edu/classification.php" rel="nofollow noreferrer"><strong>Mallet</strong></a>.</p> <p><strong>Learning Rules with Weka</strong> - If rules are what you want, Weka might be of particular interest, since it includes a <a href="http://nlp.stanford.edu/nlp/javadoc/weka-3-2/weka.classifiers.j48.PART.html" rel="nofollow noreferrer"><strong>rule set based learner</strong></a>. You'll find a tutorial on using Weka for text categorization <a href="http://weka.wikispaces.com/Text+categorization+with+WEKA" rel="nofollow noreferrer"><strong>here</strong></a>.</p>
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    1. CORe the bit on partial table + default; I was thinking of a solution along that line but that takes it one further. Rather than have a user define classification rules, they just classify specific examples and then the machine learning defines the rules from that. This has the advantage that by hooking where misclassified data gets reassigned, I can grow the rules without anyone needing to do anything but say "Itme A goes to Mr. B".
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    2. CO+1 for the simple 'no-moving-parts' LuT suggestion (which might work, might not--no way to tell w/o more info).
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