Hello,

I have a large database of resumes (CV), and a certain table skills grouping all users skills.

inside that table there's a field skill_text that describes the skill in full text.

I'm looking for an algorithm/software/method to extract significant terms/phrases from that table in order to build a new table with standarized skills..

Here are some examples skills extracted from the DB :

• Sectoral and competitive analysis
• Business Development (incl. in international settings)
• Specific structure and road design software - Microstation, Macao, AutoCAD (basic knowledge)
• Creative work (Photoshop, In-Design, Illustrator)
• checking and reporting back on campaign progress
• organising and attending events and exhibitions
• Development : Aptana Studio, PHP, HTML, CSS, JavaScript, SQL, AJAX
• Discipline: One to one marketing, E-marketing (SEO & SEA, display, emailing, affiliate program) Mix marketing, Viral Marketing, Social network marketing.

The output shoud be something like :

• Sectoral and competitive analysis
• Business Development
• Specific structure and road design software -
• Macao
• AutoCAD
• Photoshop
• In-Design
• Illustrator
• organising events
• Development
• Aptana Studio
• PHP
• HTML
• CSS
• JavaScript
• SQL
• AJAX
• Mix marketing
• Viral Marketing
• Social network marketing
• emailing
• SEO
• One to one marketing

As you see only skills remains no other representation text.

I know this is possible using text mining technics but how to do it ? the database is realy large.. it's a good thing because we can calculate text frequency and decide if it's a real skill or just meaningless text... The big problem is .. how to determin that "blablabla" is a skill ?

thanks