[ACT-R-users] similarity

[Ayman Farahat]

Hello
The apple example is interesting because it illustrates how the corpus (or
domain knowledge) can influence similarity.  My feeling is that if we asked
Steve Jobs (or for that matter a random person in the bay area) about the
most similar term to apple, MAC would come up very high.
An interesting example to try is "entropy". You will see that the similarity
server captures the two senses of the term; the information theory and
thermodynamics. 
The reason why we get better results with entropy has more to do with the
corpus than with technique.
Ayman

> From: Peter Pirolli <pirolli at parc.com>
> Date: Fri, 17 Jun 2005 09:50:05 PDT
> To: Roy Wilson <rwilson+ at pitt.edu>, act-r-users at act-r.psy.cmu.edu
> Cc: Ayman Farahat <farahat at parc.com>, Raluca.Budiu at parc.com, royer at parc.com
> Subject: Re: [ACT-R-users] similarity
> 
> As I point out 
> in the Pirolli (2005) Cognitive Science paper, PMI is approximately the
> same thing as association strength in ACT-R. PMI (like LSA) is very good at
> generating scores that correlate well with synonym judgements (e.g., as
> tested by the TOEFL test), which might be one way to define