3 research outputs found
Learning to Order Facts for Discourse Planning in Natural Language Generation
This paper presents a machine learning approach to discourse planning in
natural language generation. More specifically, we address the problem of
learning the most natural ordering of facts in discourse plans for a specific
domain. We discuss our methodology and how it was instantiated using two
different machine learning algorithms. A quantitative evaluation performed in
the domain of museum exhibit descriptions indicates that our approach performs
significantly better than manually constructed ordering rules. Being
retrainable, the resulting planners can be ported easily to other similar
domains, without requiring language technology expertise.Comment: 8 pages, 4 figures, 1 tabl
Generating Multilingual Personalized Descriptions of Museum Exhibits - The M-PIRO Project
This paper provides an overall presentation of the M-PIRO project. M-PIRO is
developing technology that will allow museums to generate automatically textual
or spoken descriptions of exhibits for collections available over the Web or in
virtual reality environments. The descriptions are generated in several
languages from information in a language-independent database and small
fragments of text, and they can be tailored according to the backgrounds of the
users, their ages, and their previous interaction with the system. An authoring
tool allows museum curators to update the system's database and to control the
language and content of the resulting descriptions. Although the project is
still in progress, a Web-based demonstrator that supports English, Greek and
Italian is already available, and it is used throughout the paper to highlight
the capabilities of the emerging technology.Comment: 15 pages. Presented at the 29th Conference on Computer Applications
and Quantitative Methods in Archaeology, Gotland, Sweden, 2001. A version of
the paper with higher quality images can be downloaded from:
http://www.iit.demokritos.gr/~ionandr/caa_paper.pd