Re:... and it's just an implementation of old idea (Score 2)

Interesting reaction. Sorry for being vague and bogus, however I didn't think a quick post to this discussion required a full exposition of the details of the method. That is why I gave a URL for our papers. However, since you obviously 'get' the math, you probably also know that *all* artificial neural networks are instantiated in matrix algebra. So why do you grumble? I really don't see this as a valid criticism. As you saw from our papers we use a linear learning method, singular value decomposition, the general method from which principal components analysis comes. Using this method and some pretty hefty computers we can use training data that are similar in size to the amount of information a person would have learned from. Non-linear learning methods would never settle when presented with 10-20 million running words of data. Vector space models of knowledge representation are an 'old' idea, traceable back to the 'semantic differential' of Charles Osgood in the '50s. I inherited the name Latent Semantic Analysis from its originators, Landauer, Dumais,Furnas, Deerwester, and Harshman of Bellcore from their first paper/patent on using the technique for information retrieval 10 years ago. (BTW, Tom Landauer is my PhD advisor, my partner and co-inventor of the IEA.) It is not only a method for information retrieval, it is also one of several respected psychological models of memory that are neurally plausible. I suppose they could have called it 'just another application of algebra (JAAA)', but then it would be confused with all the other technology out there. I also don't see anything wrong with using an old idea to help solve an old problem. It wasn't until the last few years that computers got powerful enough to do a matrix decomposition on matrices of this size. *It is not just keyword matching.* It is primarily the dimension reduction step that makes this method significantly different from simple keyword matching. Check out the Landauer & Dumais 1994 Psychological Review paper for details if interested.