Curated list of speech and natural language processing resources

This is called Question/Answering (QA). "bedroom", "apparatments" are different entities from "Capitol Hill", "Seattle". You could do as you say, trying to understand the question based on some of the words that appears using statistics. This is a "bag of word" approach.

The general idea of NLP is not different from general computer science ie. 1) narrow the problem 2) solve it 3) try to solve a bigger problem.

The tower of sentence structure in NLP is:

- bag of word

- part of speech + named enties tagging

- dependency tagging/framing

- semantic tagging

The idea is to create templates for most common questions. Then you parse questions recognizing the named entities like "Capitol Hill", "Seattle" and commons "appartement" you can resolve the question. It's not an ordinary dictionary hash lookup since for in given template there is several "key". The value of the dictionary is the correct search method. It makes me think to multiple method dispatch which support dispatch by value.

Also something to take into account is that in the "assistant" example you give, the assistant can ask for confirmation. You don't explicitly state that you are looking to "rent" something. So the system might not recognize the question, but just guess that you talk about renting something because it's the most popular search around Capitol Hill, Seattle. You can implement a "suggest this question" feature that will feedback the "question dispatch" algorithm to later recognize this question.

This is mostly a Dynamic Programming approach. Advanced NLP pipelines use logic, probabilistic programming, graph theory or all of them ;)

The other big problems of NLP are:

- summary generation - automatic translation

Important to note is that like other systems it must be goal driven. You can start from the goal and go backward infering the previous steps or do it from the initial data and go forward. Again, it's very important to simplify. Factorize by recognizing patterns. It's the main idea regarding the theory of the mind.

Have a look at this SO question [1] I try to fully explain an example QA. Coursera NLP course is a good start.

OpenCog doesn't deal solely with NLP but gives an example of what a modern artificial cognitive assistant can be made of.

Beware that NLP is kind of loop-hole.

[1] http://stackoverflow.com/questions/32432719/is-there-any-nlp...

The search engine doesn't find because it doesn't exist. A list is better than nothing. How to get things done is more interesting. At least Coursera NLP course is good, but it's only an introduction.

Looking up ready to use NLP software you can go with Solr and that's it. What I mean is that there is way too much NLP libraries. That said, it might be because there is many ways to do it. Anyway, I really think we need scikit learn for NLP.

organization != knowledge.

1. There is the Yahoo QA dataset that might be helpful. Also you can crawl specific websites for such questions

2. semi-manually come up with templates (a grammar for the questions). You have to analyse the dataset in a unsupervised way to find out the common patterns and sanatize the results.

3. maybe step 2 is enough.

4. markov networks are useful in this context but I can be wrong

> A template should be translatable to a bunch of queries (e.g.: GraphQL format). Not the hard part I believe.

Yes once you have the templates with typed variables (named entities, adjectives, etc...) like you describe you can write the code to search for the results. I doubt GraphQL is a good solution for that problem. You can't translate the templates into a search on the fly. It's a mapping that you need to build manually or automatically.

I think in your case SQL will be fine. Have a look at https://github.com/machinalis/quepy

There are about a zillion things out there that can do that.

Including Emacs, which you could use with a bit of elisp :-)