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Semantic Text Processing Powered by Wikipedia

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Maxim Grinev
Maxim Grinev

A technical overview of our Wikipedia-based Semantic Text Analysis Technology

TechnologyBusiness
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Semantic Text Processing Powered by Wikipedia Maxim Grinev [email_address]
Technology Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],Basic Technique: Semantic Relatedness of Terms Dmitry Lizorkin, Pavel Velikhov, Maxim Grinev, Denis Turdakov Accuracy Estimate and Optimization Techniques for SimRank Computation, VLDB 2008
Terms Detection and Disambiguation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Keywords Extraction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Maria Grineva, Maxim Grinev, Dmitry Lizorkin Extracting Key Terms From Noisy and Multitheme Documents WWW2009: 18th International World Wide Web Conference
Keywords Extraction (Example) Semantic graph built from a news article " Apple to Make ITunes More Accessible For the Blind "
Advantages of the Keywords Extraction Method ,[object Object],[object Object],[object Object],[object Object]
Other Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Semantic Search & Navigation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Facets Generation
Facets Generation (cont.)
Facets Generation (cont.)
Facets Generation (cont.)
Thank You!

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Semantic Text Processing Powered by Wikipedia

Editor's Notes

  1. We've developed a new technology for semantic text analysis and semantic search. The main idea behind our technology is that we use knowledge extreacted from Wikipedia to facilitate text analysis. To recent moment Wikipedia has grown into the biggest database of concepts and their relationships that ever existed. Wikipedia is great for a number of reasons (i t provides a number of things ) : 1) Comprehensive coverage (it contains very general concepts such car, computer, government, etc and a lot of niche concepts such as new small startup companies or people known only in some mmunities)  2) Continuously brought up-to-date (it is often updated just in minutes after announcements) 3) It is well-structured (it has redirects (Ivan the Terrible redirected to Ivan IV of Russia) which is synonims, it has disambiguation pages (homonyms) which includes different meaning for a term (IBM may stands for International Business Machines or International Brotherhood of Magicians). Using Wikipedia as a big knowledge base allows us to significantly improve a number of techniques and develop new techniques that were not possible before. Here is list of techniques that we developed: Advance NLP etc It is just a list of techniques. I will explain how it all works.
  2. betweenness – how much is edge “in between” different communities modularity - partition is a good one, if there are many edges within communities and only a few between them