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Python frameworks for NLP? [closed]

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I am working on a project wherein I have to extract the following information from a set of articles (the articles could be on anything):
People Find the names of any people present, like "Barack Obama"
Topic or related tags of the article, like "Parliament", "World Energy"
Company/Organisation I should be able to obtain the names of the any companies or organisations mentioned, like "Apple" or "Google"
Is there an NLP framework/library of this sort available in Python which would help me accomplish this task?

#sel and #3kt really good answers. OP you are looking for Entity Extraction, commonly referred to as Named entity recognition. There exist many APIs to perform this. But the first question you need to ask yourself is
What is the structure of my DATA? or rather,
Are my sentences good English sentences?
In the sense of figuring out whether the data you are working with is consistently grammatically correct, well capitalized and is well structured. These factors are paramount when it comes to extracting entities. The data I worked with were tweets. ABSOLUTE NIGHTMARE!! I performed a detailed analysis on the performance of various APIs on entity extraction and I shall share with you what I found.
Here's APIs that perform fabulous entity extraction-
NLTK has a handy reference book which talks in-depth about its functions with multiple examples. NLTK does not perform well on noisy data(tweets) because it has been trained on structured data.NLTK is absolute garbage for badly capitalized words(Eg, DUCK, Verb, CHAIR). Moreover, it is slightly less precise when compared to other APIs. It is great for structured data or curated data from News articles and Scholarly reports. It is a great learning tool for beginners.
Alchemy is simpler to implement and performs very well in categorizing the named entities.It has great precision when compared to the APIs I have mentioned.However, it has a certain transaction cost. You can only perform 1000 queries in a day! It identifies twitter-handles and can handle awkward capitalization.
IMHO sPacy API is probably the best. It's open source. It outperforms the Alchemy API but is not as precise. Categorizes entities almost as well Alchemy.
Choosing which API should be a simple problem for you now that you know how each API is likely to behave according to the data you have.
EXTRA -
POLYGLOT is yet another API.
Here is a blog post that performs entity extraction in NLTK.
There is a beautiful paper by Alan Ritter that might go over your head. But it is the standard for entity extraction(particularly in noisy data) at a professional level. You could refer to it every now and then to understand complex concepts like LDA or SVM for capitalisation.

What you are actually looking for is called in literature 'Named entity Recognition' or NER.
You might like to take a look at this tutorial:
http://textminingonline.com/how-to-use-stanford-named-entity-recognizer-ner-in-python-nltk-and-other-programming-languages
One easy way of solving this problem partially this problem is using regular expressions to extract words having the patterns that you can find in this paper to extract peoples names. This of course might lead to extracting all the categories you are looking for i.e. the topics and the campanies names as well.
There is also an API that you can use, that actually gives the same results you are looking for, which is called Alchemy. Unfortunatelly no documentation is available to explain the method they use to extract the topics nor the people's names.
Hope this helps.

You should take a look at NLTK.
Finding names and companies can be achieved by tagging the recovered text, and extracting proper nouns (tagged NNP). Finding the topic is a bit more tricky, and may require some machine learning on a given set of article.
Also, since we're talking about articles, I recommend the newspaper module, that can recover those from their URLs, and do some basic nlp operations (summary, keywords).

Neural Network based ranking of documents

I'm planning of implementing a document ranker which uses neural networks. How can one rate a document by taking in to consideration the ratings of similar articles?. Any good python libraries for doing this?. Can anyone recommend a good book for AI, with python code.
EDIT
I'm planning to make a recommendation engine which would make recommendations from similar users as well as using the data clustered using tags. User would be given chance to vote for articles. There will be about hundred thousand articles. Documents would be clustered based on their tags. Given a keyword articles would be fetched based on their tags and passed through a neural network for ranking.

The problem you are trying to solve is called "collaborative filtering".
Neural Networks
One state-of-the-art neural network method is Deep Belief Networks and Restricted Boltzman Machines. For a fast python implementation for a GPU (CUDA) see here. Another option is PyBrain.
Academic papers on your specific problem:
This is probably the state-of-the-art of neural networks and collaborative filtering (of movies):
Salakhutdinov, R., Mnih, A. Hinton, G, Restricted Boltzman
Machines for Collaborative Filtering, To appear in
Proceedings of the 24th International Conference on
Machine Learning 2007.
PDF
A Hopfield network implemented in Python:
Huang, Z. and Chen, H. and Zeng, D. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering.
ACM Transactions on Information Systems (TOIS), 22, 1,116--142, 2004, ACM. PDF
A thesis on collaborative filtering with Restricted Boltzman Machines (they say Python is not practical for the job):
G. Louppe. Collaborative filtering: Scalable
approaches using restricted Boltzmann machines.
Master's thesis, Universite de Liege, 2010.
PDF
Neural networks are not currently the state-of-the-art in collaborative filtering. And they are not the simplest, wide-spread solutions. Regarding your comment about the reason for using NNs being having too little data, neural networks don't have an inherent advantage/disadvantage in that case. Therefore, you might want to consider simpler Machine Learning approaches.
Other Machine Learning Techniques
The best methods today mix k-Nearest Neighbors and Matrix Factorization.
If you are locked on Python, take a look at pysuggest (a Python wrapper for the SUGGEST recommendation engine) and PyRSVD (primarily aimed at applications in collaborative filtering, in particular the Netflix competition).
If you are open to try other open source technologies look at: Open Source collaborative filtering frameworks and http://www.infoanarchy.org/en/Collaborative_Filtering.

Packages
If you're not committed to neural networks, I've had good luck with SVM, and k-means clustering might also be helpful. Both of these are provided by Milk. It also does Stepwise Discriminant Analysis for feature selection, which will definitely be useful to you if you're trying to find similar documents by topic.
God help you if you choose this route, but the ROOT framework has a powerful machine learning package called TMVA that provides a large number of classification methods, including SVM, NN, and Boosted Decision Trees (also possibly a good option). I haven't used it, but pyROOT provides python bindings to ROOT functionality. To be fair, when I first used ROOT I had no C++ knowledge and was in over my head conceptually too, so this might actually be amazing for you. ROOT has a HUGE number of data processing tools.
(NB: I've also written a fairly accurate document language identifier using chi-squared feature selection and cosine matching. Obviously your problem is harder, but consider that you might not need very hefty tools for it.)
Storage vs Processing
You mention in your question that:
...articles would be fetched based on their tags and passed through a neural network for ranking.
Just as another NB, one thing you should know about machine learning is that processes like training and evaluating tend to take a while. You should probably consider ranking all documents for each tag only once (assuming you know all the tags) and storing the results. For machine learning generally, it's much better to use more storage than more processing.
Now to your specific case. You don't say how many tags you have, so let's assume you have 1000, for roundness. If you store the results of your ranking for each doc on each tag, that gives you 100 million floats to store. That's a lot of data, and calculating them all will take a while, but retrieving them is very fast. If instead you recalculate the ranking for each document on demand, you have to do 1000 passes of it, one for each tag. Depending on the kind of operations you're doing and the size of your docs, that could take a few seconds to a few minutes. If the process is simple enough that you can wait for your code to do several of these evaluations on demand without getting bored, then go for it, but you should time this process before making any design decisions / writing code you won't want to use.
Good luck!

If I understand correctly, your task is something related to Collaborative filtering. There are many possible approaches to this problem; I suggest you follow the wikipedia page to have an overview of the main approaches you can choose.
For your project work I can suggest looking at Python based intro to Neural Networks with a simple BackProp NN implementation and a classification example. This is not "the" solution, but perhaps you can build your system out of that example without the need for a bigger framework.

You might want to check out PyBrain.

The FANN library also looks promising.

I am not really sure if a neural networks are the best way to solve this. I think Euclidean Distance Score or Pearson Correlation Score combined with item or user based filtering would be a good start.
An excellent book on the topic is: Programming Collective Intelligence from Toby Segaran

Learning Algorithms [duplicate]

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Learning efficient algorithms
I recently came across an problem that was solved by applying the correct algorithm: Calculating plugin dependencies
While I was eventually able to understand the logic of the prescribed algorithm, it was not an easy task for me. The only reason I was able to come up with code that worked was because of the logic example on the wikipedia page.
Being entirely self taught, without any CS or math background, I'd like to at least get some practical foundation to being able to apply algorithms to solve problems.
That said, are there any great books / resources (something akin to 'algorithms for dummies') that doesn't expect you have completed college Algebra 9 or Calculus 5 that can teach the basics? I don't expect to ever be a wizard, just expand my problem solving tool-set a little bit.
Doing an amazon search turns up a bunch of books, but I'm hoping you guys can point me to the truly useful resources.
The only language I have any real experience with is Python (a tiny bit of C) so whatever I find needs to be language agnostic or centred around Python/C.

"Art of Computer Programming" by Donald Knuth is a Very Useful Book.

A great book is "Introduction to Algorithms" by Cormen, Leiserson, Rivest and Stein.
Probably not the easiest one but it is very good indeed.

I found useful for myself the following sources:
"Analysis of Algorithms : An Active Learning Approach" by Jeffrey J. McConnell;
"Python Algorithms: Mastering Basic Algorithms in the Python Language"(Expert's Voice in Open Source) by Magnus Lie Hetland. - this books seems to me to be a very like a previous book but from python developer point of view;
http://en.wikipedia.org/wiki/Structure_and_Interpretation_of_Computer_Programs

Steve Skiena's Algorithm Design Manual is very good. It doesn't assume very much background knowledge, and covers several important topics in algorithms.

Personally I found Algorithms and Complexity to be super helpful. I'm also without CS degree or anything.

Programming Neural Networks with Python?

I'm a College student (Economics) and I want to program some monetary models using Neural Networks. I want those models to be able to predict future values of some variables using economic data, but I really don't know how to "model" the program itself. Is there any good Python module for that? I mean, a module for NN and a module for economic analysis?
P.S.: I'm using Python 3.x, but I can switch to 2.7.x if needed...

There is also PyBrain. I didn't work with any library yet, but had some time to investigate the documentation. It looks like PyBrains has the simplest interface, compared to the available feature set.
EDIT
I have now (Dec 2010) some practical experience with PyBrain and like it very much.

I've played with ffnet a little. PS - It was a pain to install.
"Feed-forward neural network for python"
http://pypi.python.org/pypi/ffnet/0.6

For large neural networks, you might want to consider GPU-accelerated libraries.
Our own library CUV comes to mind, as well as for example theano. CUV has python bindings, theano actually generates C++/CUDA code.

Google yields at least four different Python neural network implementations; in particular, bpnn.py looks good just for its simplicity.
Or were you looking for an explanation of neural networks?

pyfann, is fast and well documented: http://leenissen.dk/fann/wp/

Any Naive Bayesian Classifier in python? [closed]

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I have tried the Orange Framework for Naive Bayesian classification.
The methods are extremely unintuitive, and the documentation is extremely unorganized. Does anyone here have another framework to recommend?
I use mostly NaiveBayesian for now.
I was thinking of using nltk's NaiveClassification but then they don't think they can handle continuous variables.
What are my options?

The scikit-learn has an implementation of Gaussian naive Bayesian classifier. In general, the goal of this library is to provide a good trade off between code that is easy to read and use, and efficiency. Hopefully it should be a good library to learn of the algorithms work.

This might be a good place to start. It's the full source code (the text parser, the data storage, and the classifier) for a python implementation of of a naive Bayesian classifier. Although it's complete, it's still small enough to digest in one session. I think the code is reasonably well written and well commented. This is part of the source code files for the book Programming Collective Intelligence.
To get the source, click the link, dl and unpack the zip, from the main folder 'PCI_Code', go to the folder 'chapter 6', which has a python source file 'docclass.py. That's the complete source code for a Bayesian spam filter. The training data (emails) are persisted in an sqlite database which is also included in the same folder ('test.db') The only external library you need are the python bindings to sqlite (pysqlite); you also need sqlite itself if you don't already have it installed).

If you're processing natural language, check out the Natural Language Toolkit.
If you're looking for something else, here's a simple search on PyPI.
pebl appears to handle continuous variables.

I found Divmod Reverend to be the simplest and easiest to use Python Bayesian classifier.

I just took Paul Graham's LISP stuff and converted to to Python
http://www.paulgraham.com/spam.html

There’s also SpamBayes, which I think can be used as a general naive Bayesian clasisfier, instead of just for spam.