<?xml version="1.0" standalone="yes"?>
<Paper uid="C96-2143">
  <Title>A Computational Model for Generating Referring Expressions in a Multilingual Application Domain</Title>
  <Section position="2" start_page="0" end_page="0" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> An automatic generation system that is to produce good quality texts has to include effective algorithms for choosing the linguistic expressions referring to the domain entities. The expressions have to allow the reader to easily identify the referred objects, avoiding ambiguities and unwanted implications. They have to conform to the expectations of the reader according to his evolving flow of attention and they have to contribute to the cohesion of the text.</Paragraph>
    <Paragraph position="1"> In this paper, we describe a component, developed inside the GIST project 1, building referring  dustrial partners -IRST (Trento, Italy), ITRI (University of Brighton, Great Britain), OFAI (Vienna, Austria), Quinary (Milano, Italy), Universidade Complutense de Madrid (Spain)- as well as two user groups collaborating actively to the specification and evaluation of the system -INPS (the Italian National Security Service) and the Autononm Province of Bolzano.</Paragraph>
    <Paragraph position="2"> expressions for automatically generated multilingum (English, German, Italian) instructions in the pension domain. The overall decision making mechanism for the referring expressions choices is based on the theoretical investigations of Martin (Martin, 1992), for which we propose a possible implementation. The implemented model proved to be particularly suitable to work in a multilingum domain. For the generation of pronouns we define an extension of the Centering Model exploiting the contextual information provided by the rhetorical structure of discourse (Not and Zancanaro, 1996).</Paragraph>
    <Paragraph position="3"> At every stage of the referring expressions generation process issues raised by multilinguality are considered and dealt with by means of rules customized with respect to the language. In section 2 we first present the results of observations made on the corpus texts with the aim of identifying the typical referring expressions occurring in our domain. Section 3 details the solutions implemented in the GIST system. Specifications for the implementation are given in terms of data structures and required algorithms.</Paragraph>
  </Section>
class="xml-element"></Paper>