Develop Reference

Obtain relationship between words of a sentence

I am working on a project based something on natural language understanding.
So, what I am currently doing is to try and reference the pronouns to their respective antecedents, for which I am trying to build a model. I have worked out the basic part of it, but to complete the task, I need to understand the narrative of the sentence. So what I want is to check whether the noun and object are associated with each other by the verb using an API in python.
Example:
method(laptop, have, operating-system) = yes
method(program, have, operating-system) = No
method("he"/"proper_noun", play, football) = yes
method("he"/"proper_noun", play, college) = No
I've heard about nltk's wordnet API, but I am not sure whether I can use it to perform the same. Can it be used?
Also, I am kind of on a clock.
Any suggestions are welcome and appreciated.
Notes: I am using parsey-mcparseface to break the sentence. I could do the same with nltk but P-MPF is more accurate.
** Why isn't there an NLU tag available? **
Edit 1:
Thanks to alexis, The thing I am trying to do is called "Anaphora Resolution".

The name for what you want is "anaphora resolution", or "coreference resolution". It's a hard problem (probably harder than you realize-- nlp tasks are like that), so unless your purpose is just to learn, I recommend you try some existing solutions. I don't know of an anaphora resolution module in the nltk itself, but you can find it as part of the Stanford CoreNLP suite.
See this question about how to interface to it from the nltk. (I haven't tried it myself).

Can some one explain how transliteration works?

I am new to programming, and I am trying to understand transliteration - like the Google Input Tools that will allow the user to type from one language to another language.
How does transliteration work? Specifically, if I am translating from English to Hindi or English to Russsian, do I need to incorporate a dictionary of words for English, Hindi and Russian languages?
Does any one know of any tutorials showing how to write the code for transliteration? I have tried searching, but no luck.
Also, does the code have to be in JavaScript/JQuery (client side code)? My project is Python/django. Can I write the transliteration code in python/dgango?
Thanks.

Direct dictionary-to-dictionary automatic translation produces poor results due to differences in grammar and the presence of idiomatic sentences. The starting point in python, in my experience, should be NLTK (Natural Language ToolKit) libraries and tutorials.
Then, trying to provide you a working example you may start from here:
Machine Translation using babelize_shell() in NLTK
Translating human languages in Python
Google is your friend
Bing is your friend
The use of javascript/jquery depends on the UI you are planning, maybe you want to trigger an automatic translation after a few key pressed, or onblur or onchange in a input tag but is not relevant for the translation itself.
The process of translating is also really resource consuming, so I discourage you to do it inside a django view. My suggestion is to not reinvent the wheel, and use some already existing API like google or bing ones.

I found that the better search term is Input Method Editor not transliteration.
There is a project on github here: https://github.com/wikimedia/jquery.ime that deals with IME's and transliteration here.
I hope that this helps some one.

The typical way of implementing transliteration is to use a mapping dictionary. An example of this can be seen in the mapping.py file for the CyrTranslit Python package.

Word translation usages a database to convert English word into Hindi Word.
Some apps are based on this concept like:
English to Hindi Dictionary

code using nltk and python

I want a code for tagging idioms in a given sentence or text using NLTK and Python.

Depends what you mean by an "idiom". Joe's suggestion of POS tagging is probably a good start - and might be what you are really after. If so, go read "Natural Language Processing with Python" by Bird et al. It is published by O'Reilly but is also available online under a Creative Commons license. This will get you started with POS tagging. It also has a good review of NLTK's abilities. For example, can some "Named Entity Recognition" techniques be adapted to do what you want? Or perhaps what you want is simply too difficult. I suspect the latter is the case (as implied by Rafi) but you will find that out in your journey. Perhaps you'll develop something new during your journey, in which case I hope you give back to the NLTK community.

Does NLTK have a tool for dependency parsing?

I'm building a NLP application and have been using the Stanford Parser for most of my parsing work, but I would like to start using Python.
So far, NLTK seems like the best bet, but I cannot figure out how to parse grammatical dependencies. I.e. this is an example from the Stanford Parser. I want to be able to produce this in NTLK using Python from the original sentence "I am switching to Python.":
nsubj(switching-3, I-1)
aux(switching-3, am-2)
prep_to(switching-3, Python-5)
Can anyone give me a shove in the right direction to parse grammatical dependencies?

NLTK includes support for using the MaltParser, see nltk.parse.malt.MaltParser.
The pretrained English model for the MaltParser that's available here parses to the Stanford basic dependency representation. However, you would still need to call Stanford's JavaNLP code to convert the basic dependencies to the CCprocessed representation given above in your example parse.

Automatically determine the natural language of a website page given its URL

I'm looking for a way to automatically determine the natural language used by a website page, given its URL.
In Python, a function like:
def LanguageUsed (url):
#stuff
Which returns a language specifier (e.g. 'en' for English, 'jp' for Japanese, etc...)
Summary of Results:
I have a reasonable solution working in Python using code from the PyPi for oice.langdet.
It does a decent job in discriminating English vs. Non-English, which is all I require at the moment. Note that you have to fetch the html using Python urllib. Also, oice.langdet is GPL license.
For a more general solution using Trigrams in Python as others have suggested, see this Python Cookbook Recipe from ActiveState.
The Google Natural Language Detection API works very well (if not the best I've seen). However, it is Javascript and their TOS forbids automating its use.

This is usually accomplished by using character n-gram models. You can find here a state of the art language identifier for Java. If you need some help converting it to Python, just ask. Hope it helps.

Your best bet really is to use Google's natural language detection api. It returns an iso code for the page language, with a probability index.
See http://code.google.com/apis/ajaxlanguage/documentation/

There is nothing about the URL itself that will indicate language.
One option would be to use a natural language toolkit to try to identify the language based on the content, but even if you can get the NLP part of it working, it'll be pretty slow. Also, it may not be reliable. Remember, most user agents pass something like
Accept-Language: en-US
with each request, and many large websites will serve different content based on that header. Smaller sites will be more reliable because they won't pay attention to the language headers.
You could also use server location (i.e. which country the server is in) as a proxy for language using GeoIP. It's obviously not perfect, but it is much better than using the TLD.

You might want to try ngram based detection.
TextCat DEMO (LGPL) seems to work pretty well (recognizes almost 70 languages). There is a python port provided by Thomas Mangin here using the same corpus.
Edit: TextCat competitors page provides some interesting links too.
Edit2: I wonder if making a python wrapper for http://www.mnogosearch.org/guesser/ would be difficult...

nltk might help (if you have to get down to dealing with the page's text, i.e. if the headers and the url itself don't determine the language sufficiently well for your purposes); I don't think NLTK directly offers a "tell me which language this text is in" function (though NLTK is large and continuously growing, so it might in fact have it), but you can try parsing the given text according to various possible natural languages and checking which ones give the most sensible parse, wordset, &c, according to the rules for each language.

There's no general method that will work solely on URLs. You can check the top-level domain to get some idea, and look for portions of the URL that might be indicative of a language (like "en" or "es" between two slashes), and assume anything unknown is in English, but it isn't a perfect solution.
So far as I know, the only general way to determine the natural language used by a page is to grab the page's text and check for certain common words in each language. For example, if "a", "an", and "the" appear several times in the page, it's likely that it includes English text; "el" and "la" might suggest Spanish; and so on.

In Python, the langdetect package (found here) can do this.
It is based on Googles automatic language detection and supports by default 55 languages.
It is installed by using
pip install langdetect
And then for example running
from langdetect import detect
detect("War doesn't show who's right, just who's left.")
detect("Ein, zwei, drei, vier")
Will return 'en' and 'de' respectively.