The problem with the second example you posted, is there is almost no structure to it. It doesn't even have a consistent ordering of rows for each record.
I think this is where Perl shines, so I went ahead and wrote up a prototype. ( took me about an hour )
I don't think there exists a tool that would be any simpler, that would get as decent a result. ( ...
pyparsing is really a powerful tool. As the name implies, it's a module for Python. It's like zooming out one step from regexp. There are many examples at http://pyparsing.wikispaces.com/Examples
Here's another example which also makes use of regexp, in case you also want to use that for more exact pattern matching.
The Los Angeles Times did some very interesting work using natural language processing (NLP) to analyze their archival recipes and convert them into structured data.
An interview with the team ran on OpenNews Source, and Anthony Pesce wrote more (including some code samples) on the LAT Data Desk blog.
There is a comparison of different weather forecast services here:
Maybe this helps to select the best service for your requirements.
JSON is a simple text based data format than can be converted to arrays or any structured data to access it in ...
I wrote Wik2dict a decade ago to turn MW database dumps into the dict format. It's python code. Could help you figuring out some things.
The best way to convert this xml into mysql is by using mediawiki's xml import functionality.
If you don't have a good reason to import the dump into mysql it's better to avoid it as it's extremely slow with such a large ...
I found some hints for the schema here https://meta.wikimedia.org/wiki/Help:Export#Export_format
To read manually the XML, try using a viewer like:
head -n [numberoflines] dump.xml (gnu/linux terminal)
I'm a python fan, so that is the path I normally take.
If the CVs are in PDF format, then I use pdftotext to convert them into .txt files (without formatting).
Once in .txt files, to find email addresses, I usually split the lines into individual strings, and then look for strings that contain the '@' character. Since that may include twitter names or ...