Regarding the grammar rules: These are obviously ambiguous and would have
to be clarified in a dialog with a chat bot.
In terms of things that actually work already, I do have a traditional
bottom-up parser that takes rules like this:
// e.g. a dame to kill for (to kill for = <to_phrase>)
<noun> + <to_phrase> = <noun>
"to" + <verb_phrase> = <to_phrase>
<to_phrase> = <adverbial_phrase>
<question_word> + <to_phrase> = <noun> // e.g. how to...
<question_word> + <yes_no_question> = <question> // e.g. What do sheep
dream about?
The rules can be augmented with a floating point number indicating how
common they are.
But once again, I find that I would actually rather express things in
English if possible.
On Mon, 4 Feb 2019 at 23:37, Stefan Reich <
[email protected]> wrote:
> Just read it again (tried). I'd like to understand the paper, the
> introduction is quite intriguing, but then it just goes over my head...
>
> Link Grammars sound interesting. However, I increasingly find myself
> dismissing any knowledge structure that is not directly based on natural
> language. I want to get rid of all the baggage of maintaining specialized
> language and actually use English for everything. So my proposal for
> grammar rules is something like this:
>
> "Subject predicate object" is a good sentence structure if subject and
> predicate match in case.
> A noun alone is not a sentence.
> The word "a" + an adjective + a noun makes a new noun.
> A question should end with a question mark.
>
> etc.
>
> Why not make a logic engine that takes rules like these and reasons about
> them? (The problem of expressing English grammar in English is obviously
> recursive, but it's not so bad, the rules can be parsed with fairly basic
> functions.)
>
> On Mon, 4 Feb 2019 at 23:21, Linas Vepstas <[email protected]> wrote:
>
>>
>>
>> On Mon, Feb 4, 2019 at 6:02 AM Stefan Reich via AGI <[email protected]>
>> wrote:
>>
>>> > Many commentators here agreed (over time) how agi development
>>> requires a radically-different approach to all other computational
>>> endeavors to date.
>>>
>>> Not sure what that means. A really good NLU will go a very long way, and
>>> then we'll have to find a new "magic learner" module that replaces neural
>>> networks, both for image/audio recognition and learning logic. I suggest
>>> evolutionary algorithms.
>>>
>>
>> Here's my "magic learner" proposal. Actually, it is much less than that;
>> it just shows how symbolic computing and neural net computing are two sides
>> of the same coin. The idea is that once you see the correspondence, then
>> you have a clear path to the kind of symbolic computing that lots of people
>> want to do, and a way of uncloaking the "black box" aspects of neural nets.
>>
>>
>> https://github.com/opencog/opencog/raw/master/opencog/nlp/learn/learn-lang-diary/skippy.pdf
>>
>> FYI, so far, everyone I have shown this to has replied by saying "I read
>> it but I skipped the math", which is an odd thing to do, since its
>> essentially a math paper. The whole point is that, if you want to
>> understand how neural nets and symbolic learning can be placed on the same
>> footing, then you have to understand how both systems work, and "skipping
>> the math" is equivalent to "skipping the actual explanation".
>>
>> (I used to have a non-technical way of explaining this, but everyone who
>> read that was underwhelmed.)
>>
>> --linas
>>
>> --
>> cassette tapes - analog TV - film cameras - you
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>>
>
>
> --
> Stefan Reich
> BotCompany.de // Java-based operating systems
>
--
Stefan Reich
BotCompany.de // Java-based operating systems
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