Extracting 'useful' information out of sentences?

Question

I am currently trying to understand sentences of this form:

The problem was more with the set-top box than the television. Restarting the set-top box solved the problem.

I am totally new to Natural Language Processing and started using Python's NLTK package to get my hands dirty. However, I am wondering if someone could give me an overview of the high-level steps involved in achieving this.

What I am trying to do is to identify what the problem was so in this case, set-top box and whether the action that was taken resolved the problem so in this case, yes because restarting fixed the problem. So if all the sentences were of this form, my life would have been easier but because it is natural language, the sentences could also be of the following form:

I took a look at the car and found nothing wrong with it. However, I suspect there is something wrong with the engine

So in this case, the problem was with the car. The action taken did not resolve the problem because of the presence of the word suspect. And the potential problem could be with the engine.

I am not looking for an absolute answer as I suspect this is very complex. What I am looking for is more rather a high-level overview that will point me in the right direction. If there is an easier/alternate way to do this, that is welcome as well.

Answer 1

Really the best you could hope for is a Naive Bayesian Classifier with a sufficiently large (probably more than you have) training set and be willing to tolerate a fair rate of false determinations.

Seeking the holy grail of NLP is bound to leave you somewhat unsatisfied.

Answer 2

Probably, if the sentences are well-formed, I would experiment with dependency parsing (http://nltk.googlecode.com/svn/trunk/doc/api/nltk.parse.malt.MaltParser-class.html#raw_parse). That gives you a graph of the constituents of a sentence and you can tell the relations between the lexical items. Later, you can extract phrases from the output of a dependency parser (http://nltk.googlecode.com/svn/trunk/doc/book/ch08.html#code-cfg2) That could help you to extract the direct object of a sentence, or the verb phrase in a sentence.

If you just want to get phrases or "chunks" from a sentence, you can try chunk parser (http://nltk.googlecode.com/svn/trunk/doc/api/nltk.chunk-module.html). You can also carry out named entity recognition (http://streamhacker.com/2009/02/23/chunk-extraction-with-nltk/). It's usually used to extract instances of places, organizations or people names but it could work in your case as well.

Assuming that you solve the problem of extracting noun/verb phrases from a sentence, you may need to filter them out to ease the job of your domain expert (too many phrases could overwhelm a judge). You may carry out a frequency analysis on your phrases, remove very frequent ones that are not usually related to the problem domain, or compile a white-list and keep the phrases that contain a pre-defined set of words, etc.