Two Tech Reports

[Paul Munro Paul Munro]

The following TRs are available and can be obtained by writing to

Ms. Susan Webreck
LIS Building
5th Floor
University of Pittsburgh
Pittsburgh  PA   15260


Learning to Represent and Understand Locative Prepositional Phrases

Cynthia Cosic and Paul Munro

Abstract:

   The spatial juxtaposition observed between any two objects can
generally be expressed by a locative prepositional phrase of the form
noun-preposition-noun.  A network is described which learns to associate
locative prepositions with the spatial relationships they express using 
back-propagation.  Since the mapping is context sensitive, input patterns
to the network include a representation of the objects: for example, the
preposition "on" expresses different relationships in the phrases "house
on lake" and "plate on table".  The network is designed such that, during the 
learning process, it must develop a small set of features for representing the 
nouns appropriately.  The problem is framed as a pattern completion task in
which the pattern to be completed consists of four components: two nouns,
a preposition, and the spatial relationship; the missing component is either 
the preposition or the spatial relationship.  After learning, the network
was analyzed in terms of [1] its ability to process novel pattern combinations, [2] clustring of the noun representations, [3] the core meanings of the prepositions
and [4] the incremental influence of 0, 1, and 2 nouns on the interpreted 
meaning of a preposition.


The above is TR IS88002

Below is TR IS88003:

Self-supervised Learning of Concepts by Single Units and
"Weakly Local" Representations

Paul Munro

Abstract:  A mathematical system, \fIself-supervised learning\fR (SSL)
is presented that describes a form of learning for high-order
"concept units" (C-units) that learn to become sensitive to categories of
stimuli associated by some feature (the concept) that they share.
Implicit in the SSL model is the assumption that each C-unit receives 
input from at least two information streams or "banks".  Under SSL, each C-unit
becomes very selective across one of the streams, the training bank; that
is, patterns in the training bank are strongly filtered by the C-unit such
that all of them are ignored, save one, or a few.  The preferred stimulus
pattern in the traing bank serves as a "seed" for concept formation, as an 
associative process causes the stimulus patterns on the other banks to drive the C-
C-unit to the extent that they are corellated with the seed stimulus in the
world.  The possibility that linguistic informationmay provide seed stimuli
suggests an approach via SSL for understanding the role of language in
concept formation.