I should have been bit more clear about the balls-in-urn example. You are given an urn that contains balls, you pick a ball, see its color, and put it back into the urn. And, you can do this any number of times. You can't mark the balls that you have picked/observed in any way, and you are supposed to determine the #balls in the urn. There could be multiple balls that look identical.
-Amruta > In the toy example, you are given an urn that contains balls. You may see > balls with different colors or there could be multiple balls with the same > color. And, you are supposed to determine (of course automatically) the > actual number of balls in the urn. e.g. if you see one red ball, and one > blue ball, you know there has to be at least 2 balls in the urn, but there > may be 2 red balls and 2 blue balls or there could be just one red ball > and one blue ball (if you see equal number of red and blue balls in your > large #sample trials). For this, he uses a probabilistic bayesian > approach, and plots probabilities (y-axis) that the urn contains some > number > of balls (x-axis). > > In the real-world problem, he showed the results of searching citeseer for > a query "Russell and Norvig" (hit Citations and not the Documents button). > Here is what I got when I did the same search -- > > -------------- > Context Doc 619 (10): Russell S, Norvig P. Artifical intelligence a > modern approach. Upper Saddle River, NJ: Prentice Hall; 1995. > > Context Doc 166 (1): RUSSELL S., NORVIG P.: Artificial > Intelligence: A Modern Approach. Prentice Hall, 1994. > > Context Doc 43 (0): Stuart J. Russell and Peter Norvig, editors. > Artificial Intelligence: A Modern Approach. Prentice Hall, 2003. > > Context Doc 23 (0): Russell, S. J. and Norvig, P. (1995). Arti > cial Intelligence: A Modern Approach. Prentice-Hall International, Inc. > > Context Doc 19 (0): S. J. Russell and P. Norvig. Artificial > Intelligence. A Modern Approach. AI. Prentice Hall, Englewood Cli#s, 1995. > > Context Doc 18 (0): Russell and Norvig, Ai: A modern approach, > Prentice Hall, 1995. > > Context Doc 11 (1): Peter Norvig and Stuart Russell. Arti cial > Intelligence: A Modern Approach. Prentice-Hall, 1995. > > Context Doc 6 (1): P. Norvig and S. Russell. Artificial > Intelligence. A Modern Approach. Prentice Hall Series in Artificial > Intelligence, 2003. > > Context Doc 4 (0): S. J. Russell and P. Norvig. Arti cial > Intelligence. A Modern Approach. Prentice-Hall, Englewood Cli s, NJ, 1995. > > Context Doc 4 (0): S. Russell and P. Norvig. Introduction to > Artificial Intelligence. Prentice Hall, 1995. > > Context Doc 3 (0): Russell, S., and Norvig, P. Artificial > Intelligence A Modem Approach. Prentice Hall, 74, 1995. > > Context Doc 3 (0): Stuart Russell and Peter Norvig. Arti cial > Intelligence. Prentice-Hall, 1995. > > ------------- > > As you can see the same book is listed multiple times, because the author > names, titles, publisher are spelled differently, or the book had > different > years/editions etc. Ideally, it is supposed to only return only one > citation, as there is just one book that Russell and Norvig wrote, and its > just one same object. Also, it should automatically detect that names such > as "Russel" etc are spelling errors, or "Stuart Russell" is same as > "S.J. Russell" etc. > > He also gave some interesting examples like -- > > "Wauchope, K. Eucalyptus: Integrating Natural Language Input with > Graphical User Interface..." > where its not clear whether "Eucalyptus" is part of the paper title, or > its part of the author's name :-) > > But, the same publication when cited as -- > > "Kennith Wauchope (1994). Eucalyptus: Integrating Natural Language..." > > is no more ambiguous! > > Thus, we want to use object representations that are minimally ambiguous, > and maximally correct (system should prefer Russell over Russel) etc. > > Their system BLOG (stands for Bayesian Logic) uses probabilistic models > for First Order Logical representations of objects, their relations > (authors > related to papers etc). And solves the problem of counting unique objects > as that of maximizing probabilities... The interesting property of this > world is that, object descriptions could be corrupt (such as spelling > errors) and ambiguous (multiple styles of citations). > > His results show that their probabilistic first-order logic model achieves > higher accuracy (lower error rate) over the current citeseer search > engine. > > Those who attended this talk, please feel free to correct/enhance my > summary, if I said something incorrectly or missed any important point. > > Enjoy, > Amruta > > ___________________________________________________________________ > Amruta Purandare [EMAIL PROTECTED] > Intelligent Systems Program http://www.cs.pitt.edu/~amruta > University of Pittsburgh (412)-657-1318 > ___________________________________________________________________ > > _______________________________________________ > nlp mailing list > [EMAIL PROTECTED] > https://lists.cs.pitt.edu/mailman/listinfo/nlp > ------------------------ Yahoo! Groups Sponsor --------------------~--> DonorsChoose. A simple way to provide underprivileged children resources often lacking in public schools. Fund a student project in NYC/NC today! http://us.click.yahoo.com/KZzaMD/.WnJAA/HwKMAA/x3XolB/TM --------------------------------------------------------------------~-> Yahoo! Groups Links <*> To visit your group on the web, go to: http://groups.yahoo.com/group/nlpatumd/ <*> To unsubscribe from this group, send an email to: [EMAIL PROTECTED] <*> Your use of Yahoo! Groups is subject to: http://docs.yahoo.com/info/terms/
