Two preprints on Language, Brain and Computation

[Gabriele Scheler]

Two preprints on Lexical Feature Learning and Narrative Understanding are
available from Gabriele Scheler's homepage:

http://www7.informatik.tu-muenchen.de/~scheler/publications.html


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1. Lexical feature Learning

Constructing semantic representations using the MDL principle

Niels Fertig and Gabriele Scheler

Words receive a significant part of their meaning from use in communicative 
settings. The formal mechanisms of lexical acquisition, as they apply to
rich situational settings, may also be studied in the limited case of corpora
of written texts.
This work constitutes an approach to deriving semantic representations for
lexemes using techniques from statistical induction. In particular, a number
of variations on the MDL principle were applied to selected sample sets and
their influence on emerging theories of word meaning explored.
We found that by changing the definition of description length for data and
theory - which is equivalent to different encodings of data and theory - we
may customize the emerging theory, augmenting and altering frequency
effects.  Also the influence of stochastic properties of the data on the size
of the theory has been demonstrated.
The results consist in a set of distributional properties of lexemes,  
which reflect cognitive distinctions in the meaning of words.   
 

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2. Narrative Understanding

Connectionist Modeling of Human Event Memorization Processes 
with Application to Automatic Text Summarization

Maria Aretoulaki, Gabriele Scheler and Wilfried Brauer

We present a new approach to text summarization from the perspective of 
neuropsychological evidence and the related methodology
of connectionist modeling.
The goal of this project is the computational modeling of 
the specific neuropsychological processes involved in 
event and text memorization and the creation of a working model 
of text summarization as a specific problem area.

Memorization and summarization are seen as inherently related processes:
linguistic material (e.g.~spoken stories or written reports) is compressed 
into a smaller unit, a {\em schema},
which conveys the most central of the states and events described, 
making extensive use of feature representations of linguistic material.

It is in this compressed form 
that the source material is ``stored'' in memory and on this 
basis it is later retrieved.
We discuss the ways whereby these schemata
are formed in memory and the associated processes of schema
selection, instantiation and change - both  in order to further 
the understanding of these processes and to promote the development 
of NLP applications concerning the automatic condensation of texts. 
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