Linking named entities in Tweets with knowledge base via user interest modeling. Author: Chen Li Bin Wang Xiaochun Yang Speaker: Annan Wei. Outline. Introduction Tweet Entity Linking KAURI Framework Experiments Conclusion. Introduction. Twitter: a popular micro-blogging platform

ByLinking Named Entities in Tweets with Knowledge Base via User Interest Modeling. KDD’13. Task. The task: is to link the named entity mentions detected from tweets with the corresponding real world entities in the knowledge Application : personalized recommendation , user interest discovery.

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Pearl’s Belief Propagation Algorithm. Exact answers from tree-structured Bayesian networks Heavily based on slides by: Tomas Singliar, tomas@cs.pitt.edu Modified by: Daniel Lowd, for UW CSE 573. Outline. What’s a polytree? Idea of belief propagation Messages and incorporation of evidence

Neural Networks. MLP for System Modeling. f (.). f (.). f (.). Back Propagation Learning Algorithm. Forward propagation. Set the weights Calculate output. Backward propagation. Calculate error Calculate gradient vector Update the weights. Neural Networks. MLP for System Modeling.

Neural Networks. Multi Layer Perceptrons. f (.). f (.). f (.). Back Propagation Learning Algorithm. Forward propagation. Set the weights Calculate output. Backward propagation. Calculate error Calculate gradient vector Update the weights. Neural Networks. Multi Layer Perceptrons.

Overview of Back Propagation Algorithm. Shuiwang Ji. A Sample Network. Forward Operation. The general feed-forward operation is:. Back Propagation Algorithm. The hidden to output weights can be learned by minimizing the error

Survey Propagation: an Algorithm for Satisfiability. A. Braunstein, M. Mezard, R. Zecchina. Hannaneh Hajishirzi : hajishir@uiuc.edu. Content. SAT Problem Warning Passing Survey Propagation Belief Propagation. SAT Problem. CNF Clause a = (z i1 z i2 … z ik )

Refining the Basic Constraint Propagation Algorithm. Christian Bessi è re and Jean-Charles R é gin. Presented by Sricharan Modali. Outline. AC3 Two refinements AC2000 AC2001 Experiments Analytical comparison of AC2001 & AC6 Conclusion. Introduction. Importance of constraint propagation

Appendix B: An Example of Back-propagation algorithm. November 2011. Figure 1: An example of a multilayer feed-forward neural network. Assume that the learning rate is 0.9 and the first training example, X = (1,0,1) whose class label is 1.

91074 – Algorithm Languages and User Interfaces. Sorting: Bubble Sort, Selection Sort, Insertion Sort, Quick Sort. Comparisons, Cost of an Algorithm. Contents. Sorting Algorithms Informal Instructions, Algorithms and Programs High and Low Level Languages Translating Between Languages