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I hope this is an answerable question. Let me give it some context:

I am a psychology student and a programmer. I'm going to look into creating a artificial intelligence in the form of a chatterbot. I've been looking around the web, and read about projects such as Eliza which was a first simple psychotherapy program, that has now actually been written in javascript (so you can see how simple this was).

I want to create a chatterbot that can converse, and that will understand semantically and grammatically a sentence, for the purpose of reciprocal communication rather than simply outputting an answer. I'm interested in ways to learn about programming with "language". I know that this is a complex question, so the answer will probably be complex too.

What programming languages/frameworks should I look into when wanting to analyze the semantics. I know of the excellent Wolfram Alpha project that has an API. But I don't want to create an artificial intelligence that can answer any random question.

To paraphrase: What frameworks / programming languages are suited to work with deconstructing linguistic units and understanding language, for the purpose of conversing? Also do you know of any projects that implement these?

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closed as off-topic by gnat, GlenH7, MichaelT, amon, Bart van Ingen Schenau Oct 19 at 16:56



There are a lot of languages capable of this sort of stuff. Natural language processing is a very difficult problem though. It's the stuff PhDs are made of. As far as candidates...Lisp, and maybe Python, seem like good options for this. –  Rig Jan 27 '12 at 16:34
Perhaps start by looking at an existing project - for example, codeproject.com/Articles/12109/… –  JohnL Jan 27 '12 at 17:12
"I hope this is an answerable question." Sorry. It's an entire field of computer science all by itself. –  Steven A. Lowe Jan 27 '12 at 18:13
@Jakob: what you want is so complex that experts are still debating whether it is even possible at all. –  Andres F. Jan 27 '12 at 20:09
@AndresF. I know, I want to be part of that debate, I think that if I can get into this debate and qualify myself through the combination of psychology + programming I can bring something new to the table. –  Jakob Jan 27 '12 at 20:36

4 Answers 4

up vote 6 down vote accepted

If you're looking for an entry point into this huge topic, there are worse places to start than the Natural Language Toolkit. The API is Python. It's well documented. The book, Natural Language Processing with Python - Analyzing Text with the Natural Language Toolkit, is even available to read online.

I don't want to mislead you. The NLTK isn't a short-cut to a convincing chatterbot. It is however

"suited to work with deconstructing linguistic units and understanding language..."

If you don't have tight deadlines, Stanford is offering a free course on natural language processing in Feb. 2012.

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Thank you very much. I'll look into this. No tight deadlines, it's a personal project to me. Hopefully in the future I'll be able to integrate it with my study though. The course sounds perfect :) –  Jakob Jan 28 '12 at 15:22

Natural language processing is incredibly difficult, because human languages are so ambiguous, and require a lot of general background context to make sense of what is being said. Consider the following sentences:

  1. "Monkeys like bananas when they wake up"
  2. "Monkeys like bananas when they are ripe"
  3. "I saw the lab technician with the microscope."

In (1) and (2) does the "they" refer to the monkeys or the bananas? Well, as a human being it's clear to me that in (1) the "they" refers to the monkeys, whereas in (2) it refers to the bananas. But how exactly did I know that? The information isn't inherent in the grammar of the sentence - it's only available because as a human being I have access to a large database of background knowledge in my brain, which tells me that bananas don't ever "wake up", and that monkeys aren't "ripe". Therefore, my brain provides the contextual information to parse the sentence properly.

In (3) we see a similar problem: Does (3) mean that I saw a lab technician who was using a microscope, or does it mean I actually used a microscope to see a lab technician? Grammatically speaking, it could mean either. But to a human being there is no ambiguity. It must mean the former, because the latter is absurd. But the only reason that I even realize that the latter is absurd is because I know what a microscope is, I know that a lab technician is a person, I know that you wouldn't use a microscope to see a person, and I know that lab technicians often work with microscopes. So my brain synthesizes all of that background knowledge, allowing me to interpret the sentence correctly.

Now imagine trying to parse sentences like these with a computer. You'd need a vast database (known as an ontology) of real-world facts and relationships to provide context, and a pretty sophisticated parser. There have been some attempts to do this in the past, but it's still a very theoretical, very research-oriented endeavor.

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"The astronomer married a star" –  Steven A. Lowe Jan 28 '12 at 7:17

So far there are at least 50 years of research in natural language understanding. You aren't going to solve it all by yourself, and choosing a programming language is about one-trillionth of the effort. Look at the effort that went into building IBM's Watson. They didn't even attempt to solve the general problem of language understanding. They only had to understand clues well enough to formulate a probable answer. Automatic translation systems are another good reference. After hundreds of man-years of effort we have useful automatic translators, but nothing good.

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That's exactly what I want to do. Thank you for the resource, I'll look into that. –  Jakob Jan 28 '12 at 2:25

Here's a helpful link: http://ai-programming.com/bot_tutorial.htm

That's where I learned a lot of techniques I'm using in my own chatterbot called Quanda Sparks. Although organizing, analyzing, and creating common language algorithmically is a complex subject, and so far no bot has passed the Turing Test (as far as I know), there are a few ways to mimic human speech.

Template Responses: This is a way to make it seem as if a robot understands by having it talk of what the human inputted. Let's say I type in "I like tacos and enchiladas." The bot responds with "What is so great about tacos and enchiladas?" How did it arrive with this response? When it sees "I like", it takes whatever follows and makes a template, in this case "tacos and enchiladas." Then it can construct a sentence with "What is so great about " + template. The robot can fool its audience without ever even know what a taco is.

Generic Responses: Give the bot lots of responses that could fit in almost any scenario. Some examples include "okay", "That's cool", "I hear you", etc. That way, if the bot has nothing to say, it still has a chance at keeping up the act.

Thesaurus: Let's say you have plenty of responses looking for the word "awesome", but the user enters in "amazing". Normally, the bot would be stumped and would have to resort to one of its generic responses; however, if you replace all instances of the word "amazing" with "awesome", it will still be able to find a good response.

Foil cheaters' plans: Users will try all they can to fool the bot into revealing its weaknesses in speech. Some things they will try: leaving the input field blank, entering gibberish, repeating the same thing twice, mimiking the bot, and putting words out of place, such as "you are a bot who". Make sure you give good responses under these circumstances. In the last example, you could check to make sure that there is a word after "who" before telling the bot the user asked a question.

Speak in context: This is the hardest part and the main reason bots fail the Turing Test. Chatterbots can only pretend to understand by giving responses that make sense. If you ask a chatterbot to remember the number 490, talk for a while, and then ask the bot what the number was, most bots will have no clue what you are talking about. Most good bots can look at least one or two lines back. Make sure you also make your bot context-sensitive otherwise.

Do not repeat responses: When you talk to a person, does he usually say "Oh, that's cool ... oh, that's cool ... oh, that's cool" like that three times in a row? No. Even if he is not really interested, he will change up the words like this: "oh, that's cool ... awesome ... that's good." Make sure your bot also waits a while before repeating itself.

Learn from mistakes: It's hard to make the robot learn by itself, but you can teach it more efficiently like this: have people converse with it. Have it record the conversation and any input it does not have a response for. Then, collect this data and you it to build more responses.

I hope these ideas can help you get started. As for what technologies you would need for this, I am programming mine in Java and storing it's knowledge base in a text document. You really don't need anything past that. I was originally going to integrate in an SQL database, but soon found it wasn't worth it. You don't need any fancy APIs. Just use your head and problem-solving ability. If you want to be able to program conversation, you need to understand how conversation works. Study real conversations and look for patterns. When you find one, integrate it.

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