14.550, Diss: Computational Ling: Pallotta "Cognitive..."

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LINGUIST List:  Vol-14-550. Mon Feb 24 2003. ISSN: 1068-4875.

Subject: 14.550, Diss: Computational Ling: Pallotta "Cognitive..."

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1)
Date:  Mon, 24 Feb 2003 13:10:34 +0000
From:  Vincenzo.Pallotta at epfl.ch
Subject:  Computational Ling: Pallotta "Cognitive Lang Engineering..."

-------------------------------- Message 1 -------------------------------

Date:  Mon, 24 Feb 2003 13:10:34 +0000
From:  Vincenzo.Pallotta at epfl.ch
Subject:  Computational Ling: Pallotta "Cognitive Lang Engineering..."

New Dissertation Abstract

Institution: Swiss Federal Institute of Technology Lausanne
Dissertation Status: Completed
Degree Date: 2003
Author: Vincenzo  Pallotta

Dissertation Title: Cognitive Language Engineering: Towards Robust
Human-Computer Interaction.

Linguistic Field: Semantics, Pragmatics, Computational Linguistics,
Applied Linguistics

Dissertation Director 1: Giovanni Coray

Dissertation Abstract:

Intelligent information processing seems to be one of the most
challenging task among those involved in human-computer interaction. A
central issue is how to model the various types of interaction among
artificial and natural entities at different levels of abstraction. On
the one hand, models of interaction are required to better understand
the communication phenomena. On the other, suitable languages and
paradigms should provide powerful frameworks for developing
computer-based applications.

In this dissertation I focus on different aspects of the second
problem, trying to develop a methodology for the design of interactive
natural language applications (e.g. from question-answering to
mixed-initiative dialogue). One of the main aspects I am concerned
with in this work is the problem of their robustness. Several methods
have been proposed for achieving robustness in natural language
understanding, but these methods are sometimes hard to scale up or
re-use in different applications. Moreover, they often concentrate on
a single linguistic level of the processing rather than offering a
global solution. I will set up a Language Engineering environment
whose goal is to combine software engineering and cognitive aspects
(e.g. aspects related to representation of a mental model of the
speaker).

Given its complexity, it is apparent that the problem can be solved
only partially. I want to stress here that the main contribution of my
work is a holistic perspective on the problem of natural language
understanding. Rather than focusing on a particular aspect of natural
language processing. I tried to benefit from the big amount of work
that has been already done in Computational Linguistics and Computer
Science merging different ideas and techniques.

In the first part of the dissertation I explore the universe of
Language Engineering in order to clarify how my contribution can be
situated. After a survey on the state of the art on robust methods in
analysis of natural language data, I focus on the role that
Computational Logic plays in relating the syntactic and semantic
analysis of natural language to its practical understanding within
specific applications.

Robustness is considered from two complementary perspectives,
borrowing the terminology from modern software engineering: robustness
"in the small" and robustness "in the large."

The first perspective is discussed while presenting an application for
the Interaction through Speech with Information Systems, where robust
semantic parsing is used to extract queries from spoken natural
language utterances.

The second perspective is examplified by the re-engineering of an
existing text analysis system using a new Language Engineering
methodology: Agent-Oriented Language Engineering.

In the second part of the thesis I discuss how cognitive aspects can
be integrated into a Language Engineering environment leading to the
notion of Cognitive Language Enginering. I tackle the difficult
problem of robust dialogue management from both a cognitive and
computational perspective. I propose two frameworks for the semantic
representation and assimilation of information into the dialogue
information state. The first framework allows us to represent and
reason about the dynamic aspects of objects and events. The second
framework is centered on the notion of mental space and it is used to
build representations of the cognitive processing of information
during communication.

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