[ACT-R-users] similarity

[Wai-Tat Fu]

Just want to add to Pete and Ayman's point about the use of PMI as Sji 
values in ACT-R. From that perspective, spreading activation will calculate 
the weighted sum of these PMI/Sji values with respect to your active goal. 
It is seldom the case that you have only one slot in your goal. In Ayman's 
example, if you have "apple", "Steve jobs", or "bay area" as slots in your 
goal, MAC will likely to be more active than fruits. This is similar to the 
use of multiple keywords when you google for some information on the web.

Just as an example, I know that a number of HCI studies (e.g., menu search) 
used questionnaires to get subjective ratings as a measure of semantic 
relevance for their model. I think the tool will be very useful for this 
kind of research.

--On Friday, June 17, 2005 10:18 AM -0700 Ayman Farahat <farahat at parc.com> 
wrote:

> 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
>
>
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