Natural language processing can be manageable if you confine yourself to a particular problem domain. For example, one of my pet projects that eventually will see light is a module to parse "natural" descriptions for dates for a cron-style scheduler. The idea is that most of these descriptions are phrases that my module will recognize will either have been copied from the synopsis or they will match some simple patterns :

• The third wednesday of the month
• The first monday after the first tuesday
• Every friday

For my case, there is no deep understanding necessary, as the only ordinal words that can occurr are first,second,third,fourth,fifth, the atomic phrases are simple, and the only composite phrases are atomic phrases concatenated by before and after. There are some implicit assumptions like that of the month is implicitly added if no month is given, and that the next date in the future is selected (that is, a date lies either in the current month or in the month after that if no absolute date has been specified).

This is by no means a module that "understands" the text given, but with my external knowledge about the supposed content, it can extract and convert the data given.

perl -MHTTP::Daemon -MHTTP::Response -MLWP::Simple -e ' ;    # The  
$d = new HTTP::Daemon and fork and getprint $d->url and exit;#spider
($c = $d->accept())->get_request(); $c->send_response( new   #in the
HTTP::Response(200,$_,$_,qq(Just another Perl hacker\n))); ' #  web
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