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<p class=MsoNormal style='text-autospace:none'><font size=2 face="Courier New"><span
style='font-size:10.0pt;font-family:"Courier New"'>We are in the process of
mapping the linguistics representations that are generated by our language
comprehension model into a situation model based semantic representation. We
are trying to do this in a representationally reasonable way within the ACT-R
architecture. The problem we face is the many-to-many mapping between words and
concepts. Individual words may map to multiple concepts, and individual
concepts may may to multiple words. Given this many-to-many mapping, we would
like to use mapping chunks to map from words to concepts. The mapping chunks
would encode a single mapping relationship (e.g. a separate mapping chunk to
map from the word "bank" to the financial institution concept; from
the word "bank" to the river bank concept; from the concept dog to
the word "dog"; from the concept dog to the word "canine").
When processing a word, the goal is to retrieve the contextually relevant
concept. We would like to accomplish this in a single retrieval, however, we do
not know how to do this given the single-level activation spreading mechanism
in ACT-R. Since there is no direct link between a word and a concept if mapping
chunks are used (i.e. there is no slot in the concept that contains the word),
the word will not spread activation to the concept. Instead, given the use of
mapping chunks, it appears that two retrievals are needed: 1) given the word,
retrieve a mapping chunk, and 2) given a mapping chunk, retrieve a concept.
Since our model of language comprehension is already slower than humans at processing
language, any extra retrievals are problematic. In fact, we have already
eliminated an extra retrieval in determining the part-of-speech of a word.
Previously, two retrievals were needed: 1) retrieve the word corresponding to
the perceptual input, and 2) given the word (and context) retrieve the
part-of-speech of the word. While we were successful in eliminating a
retrieval, the resulting word-pos chunks contain a mixture of word form
information (e.g. the letters and trigrams in the word) and pos information.
Even so, they do not yet contain any representation of phonetic, phonemic,
syllabic or morphemic information. With just letter and trigram information,
long words contain many slots. Ideally, we would like to represent letter and
trigram information independently of each other and POS information (allowing
them to interact in retrieving a word), but given the single-level activation
spreading mechanism in ACT-R doing so would necessitate multiple independent
retrievals, which would fail to capture the interaction of letter and trigram
information that leads to successful retrievals of words in the face of
variability in the perceptual form (e.g. "arispeed" should retrieve
"airspeed").<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=2 face="Courier New"><span
style='font-size:10.0pt;font-family:"Courier New"'> <o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=2 face="Courier New"><span
style='font-size:10.0pt;font-family:"Courier New"'> The fall back for
mapping words to concepts is to embed all the possible concepts as slot values
in a word and vice versa. While we consider this a representationally
problematic solution -- word and concept chunks will wind up needing many extra
slots, we do not know how else to work around the single-level activation
spread in ACT-R.<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=2 face="Courier New"><span
style='font-size:10.0pt;font-family:"Courier New"'> <o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=2 face="Courier New"><span
style='font-size:10.0pt;font-family:"Courier New"'> The primary empirical
argument against the need for multi-level activation spread in ACT-R is based
on studies which show no activation from words like "bull" to words
like "milk", even though "bull" activates "cow"
and "cow" activates "milk". Even if it is true that there
are no instances of "indirect" activation from "bull" to
"milk", this does not rule out the need for multi-level activation
spread. There is a hidden assumption that "cow" and "bull"
are directly associated, and that "cow" and "milk" are also
directly associated. Such direct associations may seem reasonable in
small-scale models addressing specific spreading activation phenomena, but they
are questionable in a larger-scale model. Do we really want to include all the
direct associates of "cow" as slot values in the "cow"
chunk, and do the same for all other chunks? <o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=2 face="Courier New"><span
style='font-size:10.0pt;font-family:"Courier New"'> <o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=2 face="Courier New"><span
style='font-size:10.0pt;font-family:"Courier New"'> We understand that the
inclusion of a multi-level activation spreading mechanism in ACT-R would be
computationally explosive. However, we would like to have the capability to
explore use of such a mechanism and to look for ways to keep it computationally
tractable. We have already dealt with the problem of computational explosion in
our word retrieval mechanism. Originally, we attempted to use a "soft
constraint" retrieval mechanism for words. All words in DM were candidates
for retrieval--the most highly activated word being retrieved. With just 2500
words in DM, the activation calculations slowed the model down considerably. To
manage retrievals in a tractable manner we implemented a disjunctive retrieval
capability combined with a new perceptual span mechanism -- the model first
tries a hard-constraint retrieval on the entire perceptual span (which is
larger than a word) using the "get-chunk" function (and chop-string
under the covers). If get-chunk succeeds (indicating that there is a chunk in
DM corresponding to the entire perceptual span) a retrieval is constructed
using the entire perceptual span as a hard constraint to retrieve the
corresponding multi-word unit in DM, if this fails, the model backs-off and
uses the first space delimited word (using chop-string) in the perceptual span
to check for a corresponding word in DM -- if a match is found with get-chunk,
a retrieval is constructed to retrieve the word. If all else fails, we
construct a retrieval that imposes a hard constraint on the first letter (this
is less than ideal, but a reasonable compromise). The overall effect is a
(nearly) soft-constraint retrieval implemented in a computationally tractable
way.<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=2 face="Courier New"><span
style='font-size:10.0pt;font-family:"Courier New"'> <o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=2 face="Courier New"><span
style='font-size:10.0pt;font-family:"Courier New"'> A similar capability
to effect multi-level activation spread in a computationally tractable manner
would be highly desirable.<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=2 face="Courier New"><span
style='font-size:10.0pt;font-family:"Courier New"'><o:p> </o:p></span></font></p>
<p class=MsoNormal style='text-autospace:none'><font size=2 face="Courier New"><span
style='font-size:10.0pt;font-family:"Courier New"'>Jerry<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span style='font-size:10.0pt;
font-family:Arial'><o:p> </o:p></span></font></p>
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