Sentence Patterns

Linas Vepstas — Tue, 08 Sep 2009 14:54:23 +0000

I’ve recently resumed work on the question-answering chatbot, and am trying to get it to comprehend a broader range of questions and statements. The “big idea” is to create a number of “sentence patterns” that the pattern matcher can recognize and respond to. The reason this is a “big” idea is because I am trying to avoid anything algorothmic or procedural — everything is to be done by specifying OpenCog hypergraphs, and NOT by writing C++ code, or scheme code (or python code…etc). The reason for working entirely with patterns and hypergraphs, rather than with C++ or scheme, is because this puts the “knowledge” of the system into a form that AI routines can manipulate it: learning algos can learn new hypergraphs; statistical algos can gather usage information on which hypergraphs get triggered, and so on. This is all easer said than done: although I’ve eliminated a fair amount of question-answering code previously written in C++, I’ve also had to write some new scheme code. Bummer.

Patten matching is now used through-out all of the OpenCog NLP pipeline, although not in a unified manner. The Link Grammar parser uses patterns (called “disjuncts”) to determine how the words in a sentence can link to one-another, thus “parsing”, or pulling the grammatical structure out of a sentence (this paper provides an excellent overview). The RelEx dependency relation extractor applies patterns on the link-grammar output to extract syntactic relations. For example, the sentence “John threw a rock” becomes

_obj(throw, ball)
_subj(throw, John)

after RelEx gets done with it. And now, there are a dozen patterns inside of OpenCog that can pick out certain kinds of questions and statements from RelEx output, and pattern-match questions to find answers to them.

For example, the new OpenCog patterns convert “The capital of France is Paris” into

capital_of(France, Paris)

and similarly, “What is the capital of France?” into

capital_of(France,what)

Treating “what” as a variable, there is yet another pattern that matches up the form of the question to the form of the answer, thus deducing that “what” must be “Paris”.

Somewhat harder is using patterns to distinguish similar from dis-similar concepts, so that sentences like “John threw a green ball” aren’t used as answers to questions such as “Did John throw a red ball?”: the word “ball” with modifier “green” has to be detected as a different entity than the word “ball” with modifier “red”: these are two different entities (called “semes” in the code). In fact, out of laziness, I’ve punted on this one: the promotion of word-instances to “semes” is done by code, rather than by pattern matching. But soon, I hope, this will change. In the meanwhile, the README file provides a more detailed discussion.

Here are some patterns that work these days:

         John threw a green ball.
         Fred threw a red ball
         Mary threw a blue rock
         who threw a ball?
Syntax pattern match found: Fred John
         who threw a red ball?
Syntax pattern match found: Fred

         Did Fred throw a ball?
Truth query determined “yes”: throw

         Did Fred throw a red ball?
Truth query determined “yes”: throw

         The color of the book is red.
         What is the color of the book?
Triples abstraction found: red

         the cat sat on the mat
         what did the cat sit on?
Triples abstraction found: mat

And here are some that don’t yet work: “Did Fred throw a green ball?” — gets no reply, because the system can’t find an answer, and doesn’t make the common-sense leap of “can’t find answer-> answer must be no”. Another common-sense problem is illustrated by: “Did Fred throw a round ball?” — the system doesn’t know that balls are round, and simply assumes that a “round ball” is some special kind of “ball”. Oh well. There’s work to be done.

You can try out the chatbot yourself (when its up, and not broken!) on the IRC chat channel #opencog on the freenode.net chat servers.

– Linas Vepstas

proto-chatbot at last!

Linas Vepstas — Tue, 28 Apr 2009 02:22:07 +0000

Hands-on tutorials are planned for the next month or so; we’ve already had a few on PLN, and my turn is coming up, for the opencog NLP pipeline. So I thought I’d wire up a cute demo for the occasion: a rough, crude IRC chatbot, “La Cogita”. It can answer simple questions about straightforward statements. Nothing fancy … it doesn’t do any reasoning at all … but it can work off of the basic syntactic structure of English sentences to find answers. Thus, for example:

Mary ate a mango Hello linas, parsing ... linas, you made a statement: Mary ate a mango what did Mary eat? Hello linas, parsing ... linas, you asked a question: what did Mary eat? The answer to your question is: mango

Its meant to be a demo of the basic NLP pipeline within OpenCog. It takes input text, runs it throught the Link Grammar + RelEx parser, imports the results into the OpenCog atomspace, sucks in a small common-sense database, and waits for questions to be posed. The common-sense database is derived from MIT’s ConceptNet (OpenMind/CommonSense project), and so one can have interactions like the following:

cogita-bot: what is a saxophone? Hello linas, parsing ... linas, you asked a question: what is a saxophone? No answer was found to your question. hmm cogita-bot: what is an instrument? Hello linas, parsing ... linas, you asked a question: what is an instrument? The answer to your question is: woodwind r bass harmonica An_Oboe Oboe megaphone saxophone chronometer drum scale ukulele cymbal instrument Heh. Complete with assorted linguistic garbage

You get the idea. Don’t ask it anything more complicated than the above examples: it will fail to find any answer. Again, it does no reasoning at all. Its as thick as a brick. You can test-drive it at the #opencog channel on the freenode.net IRC network. Assuming its not down for development.

Next up: wire in NLGen for natural-language output, and start taking baby steps in actual reasoning. Anyway, I’m pretty excited, as this means that a lot of the basic bits&pieces are working, and I can now dive into the deep end, and start working on the hard stuff.

– Linas Vepstas

OpenCog Brainwave » chatbot

Sentence Patterns

proto-chatbot at last!