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Semantic Vectors A Scalable Open Source Package and Online Technology Management Application

LREC Conference 28 th May, 2008. Semantic Vectors A Scalable Open Source Package and Online Technology Management Application. Dominic Widdows Google, Inc. widdows@google.com. Kathleen Ferraro University of Pittsburgh kaf1@pitt.edu. Software is often hard to use / unreliable

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Semantic Vectors A Scalable Open Source Package and Online Technology Management Application

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  1. LREC Conference 28th May, 2008 Semantic VectorsA Scalable Open Source Package and Online Technology Management Application Dominic Widdows Google, Inc. widdows@google.com Kathleen Ferraro University of Pittsburgh kaf1@pitt.edu

  2. Software is often hard to use / unreliable t (fiddling with computers) >> t (analysing data) Does it scale? Moore's Law of Data – any algorithm more costly than linear hurts more every day! What is it for? Systems / components Interesting (science) / useful (engineering) Natural Language Software Engineering – Three Problems

  3. Count how many times words occur in some context Term–Document matrix LSA (“Latent Semantic Analyis”) Or count how many times words cooccur with one another HAL (“Hyperspace Analogue to Language”) Normally we reduce dimensions somehow SVD, NNMR, LDA. Many uses IR, WSD, OL / LA, DS / TDT, OCIM, DC, ... , Acronym Resolution. Semantic Vector Models

  4. http://semanticvectors.google.com/ Created by University of Pittsburgh and MAYA Design All Java (with some Perl / Python / php wrappers) Maintained by Google 20% project + other contributors BSD license – you can use it. Nearly 1000 downloads Developer group, Wiki, mailing list, ... “Child of Infomap” with lessons learned Semantic Vectors Package

  5. 100% Java Dependencies include Apache Lucene Installation User Download jarfiles Add to your $CLASSPATH Assemble a corpus (example provided) Type “java pitt.search.semanticvectors.BuildModel” Developer Install SVN, Ant Checkout source (Google code helps) Install JUnit for testing We have had no reports of difficulty yet! Challenge 1: Make it Easy!

  6. Dimension reduction and parallelization are key Random Projection Geometric alternatives: SVD (orthogonality) Probabilistic alternatives: PLSA, LDS (generative models) Sparse Random Vectors, e.g. [0,0,0,1,0,-1,0,0,0,-1,0,0,0,0,1,0,0] [0,-1,0,0,0,1,0,0,0,0,1,0,0,0,0,-1,0] On average, dot products are nearly zero, so vectors are nearly orthogonal. Approximate benefits of SVD, with none of the cost! Believed to be trivially parallelizable and incremental (TODO) Challenge 2: Make it Scale!

  7. Hardest of the three problems Technology Matching at UPitt http://real.hsls.pitt.edu/ Matches technology disclosures to documents harvested from company websites Traditionally needs much more than keywords Does your data meet your needs? Challenge 3: Make it Useful!

  8. Negation, Disjunction “Quantum” / Vector Logic Translation Bilingual Vector Models Semantic Vector Products Direct, Tensor, Convolution, Subspace Clustering kMeans Context Window Approach (HAL) Thanks to Trevor Cohen, ASU, Biomedical Informatics Features and Demos ...

  9. Geometry, Probability, Logic Intersection “That one term should be included in another as in a whole is the same as for the other to be predicated of all of the first.” Prior Analytics (Bk I, Ch 1) The equations work ... does the method? “It is the mark of an educated man to look for precision in each class of things just so far as the nature of the subject admits; it is evidently equally foolish to accept probable reasoning from a mathematician and to demand from a rhetorician scientific proofs.” Nicomachean Ethics(Bk I, Ch 3) What do people do? “By nature animals are born with the faculty of sensation, and from sensation memory is produced in some of them, though not in others ... Now from memory experience is produced in humans; for the several memories of the same thing produce finally the capacity for a single experience.” Metaphysics(Bk I, Ch 3) Mathematics, Technology, Cognition

  10. ELRA and the LREC conference Developers of Java, Lucene, Ant, Junit, ... Google, University of Pittsburgh Harris, Firth, Van Rijsbergen, Salton, McGill, Landauer, Deerwester, Berry, Dumais, Schutze, Lund, Burgess, Sahlgren, Kahlgren, Kaufmann, Dorow, Cederberg, Hofmann, Kanerva, Plate, Papadimitriou, McArthur, Bruza, ... Many Thanks ...

  11. http://infomap.stanford.edu/book Introduction to Vectors, WordSpace, Quantum Logic, etc. A few for sale here ... 150 د.م. Download the package ... Google(Semantic Vectors) And finally ...

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