Reminds me of Explicit Semantic Analysis (ESA), which in some circumstances appears to perform as well or better compared to missing data approaches (LSA, topic modelling).
http://code.google.com/p/research-esa/ jds ________________________________________ From: Dan Brickley [[email protected]] Sent: Saturday, May 19, 2012 3:50 PM To: [email protected] Subject: Wikipedia things/strings dataset Just noticed this handy-looking dataset, http://googleresearch.blogspot.com/2012/05/from-words-to-concepts-and-back.html "From Words to Concepts and Back: Dictionaries for Linking Text, Entities and Ideas" Excerpt, "How do we represent concepts? Our approach piggybacks on the unique titles of entries from an encyclopedia, which are mostly proper and common noun phrases. We consider each individual Wikipedia article as representing a concept (an entity or an idea), identified by its URL. Text strings that refer to concepts were collected using the publicly available hypertext of anchors (the text you click on in a web link) that point to each Wikipedia page, thus drawing on the vast link structure of the web. For every English article we harvested the strings associated with its incoming hyperlinks from the rest of Wikipedia, the greater web, and also anchors of parallel, non-English Wikipedia pages. Our dictionaries are cross-lingual, and any concept deemed too fine can be broadened to a desired level of generality using Wikipedia's groupings of articles into hierarchical categories. The data set contains triples, each consisting of (i) text, a short, raw natural language string; (ii) url, a related concept, represented by an English Wikipedia article's canonical location; and (iii) count, an integer indicating the number of times text has been observed connected with the concept's url. Our database thus includes weights that measure degrees of association. " [...] I figured this should be of interest to a good few Mahout users, so passing it along... cheers, Dan
