Changed file name, added contextual inference example

Split into two pull requests: https://github.com/opencog/opencog/pull/765
+1 on the comments.

I am reminded that in certain parts of the linguistics community, there are
active efforts to try to carefully characterize all English verbs, as to
what subjects, objects, agents, patients etc. they can all take. Current
approaches seem to involve long, tedious, laborious, manual processes. At
least one such project uses phone apps (games) where the user plays games
with non-sense sentences; researchers gather the game data to figure out
how verbs work.

So I'm thinking that one of the sooner efforts we'll have to do, after the
summer is over, (after r2l+pln sort-of works) is to try to automatically
learn how to interpret verbs, specifically, to tell apart the 'Ben eats
worms' from 'Ben appears ridiculous' cases. (but not from games, just from
text+embodiment)

I claim again that this should be doable via various machine learning
techniques, but it won't be easy, but we should focus on it because we
can't afford to hand-build detailed verb-use dictionaries.

--linas

On 4 June 2014 19:48, bgoertzel <notifications@github.com> wrote:

>
> Sebastian, You raise a good point regarding the use of frame-based versus
> non-frame-based representations.
>