True Knowledge - The Internet Answer Engine

http://www.trueknowledge.com/

Just a beta version but very impressive about what it can do for us.

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Cheers,
Vu

IBM PhD Fellowship

https://www.ibm.com/developerworks/university/phdfellowship/

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Cheers,
Vu

Open source search engine

The below is some collected information relevant to up-to-date open source search engine toolkits:

1) Lucene: http://lucene.apache.org/ (Java)
--> CLucene: http://sourceforge.net/projects/clucene/ (C++)

2) Minion: https://minion.dev.java.net
3) Galago: http://www.galagosearch.org/

4) Xapian: http://xapian.org/ (C++)

5) http://www.searchenginecaffe.com/2007/03/open-source-search-engines-in-java-and.html (very informative)

6) Minion vs. Lucene: http://blogs.sun.com/searchguy/entry/minion_and_lucene_query_languages

7) Search Engine Wrapper (Yee Fan Tan - NUS): http://wing.comp.nus.edu.sg/~tanyeefa/downloads/searchenginewrapper/

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Cheers,
Vu

Bayesian Inference with Tears

Will plan to read this article to understand more about Bayesian inference applied to NLP.
Link: http://www.isi.edu/natural-language/people/bayes-with-tears.pdf
(by Kevin Knight)

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Cheers,
Vu

Notes in machine learning

http://www.ics.uci.edu/~welling/classnotes/classnotes.html

Think such notes are very useful for me to learn more about topics in machine learning.

Useful datasets for Machine Learning: http://archive.ics.uci.edu/ml/

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Cheers,
Vu

Markov Logic Networks

Markov Logic Networks, a combination of First Order Logic and Markov Networks, is a new graphical model which will be very important for AI modeling in the future. Prof. Pedro Domingos at Univ. of Washington is a pioneer in this field. There are some major references given by him:

1) New book "Markov Logic - An Interface Layer for AI".
Another editorial book: "Integrating Logic and Statistics: Novel Algorithms in Markov Logic Networks" by Marenglen Biba

2) The course about Markov Logic Networks given by Prof. Pedro Domingos at Univ. of Washington.

3) The article "What's missing in AI - The Interface Layer".

4) Alchemy - Open source AI: http://alchemy.cs.washington.edu/

I wonder whether some NLP problems can benefit from such a new model.

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Cheers,
Vu

Graphical Models in a Nutshell

The paper by Prof. Daphne Koller :
http://robotics.stanford.edu/~koller/Papers/Koller+al:SRL07.pdf

MUST read this paper to understand the underlying principles behind graphical models before proceeding to investigate more!

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Cheers,
Vu