Moving from data to text using causal statements in explanatory narratives

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Data-to-text natural language generation techniques
do not currently impart deep meaning in their output
and leave it to an expert user to draw causal inferences.
Frequently, the expert is adding meaning that would be present
in data sources that could be made available to the NLG system.
As the system is intended to convey as much information as
possible, it seems counterintuitive to require the user to add
meaning that could already have been included in the systems
output. In this paper, we introduce our concept of using a
reasoning engine to draw causal inferences about the data and
then expressing them in an explanatory narrative.
Original languageEnglish
Title of host publicationProceedings of the 10th UK Workshop on Computational Intelligence (UKCI)
PublisherIEEE Explore
Number of pages6
Publication statusPublished - 2010
EventProceedings of the 10th UK Workshop on Computational Intelligence (UKCI) - Essex, United Kingdom
Duration: 8 Sep 201010 Sep 2010

Conference

ConferenceProceedings of the 10th UK Workshop on Computational Intelligence (UKCI)
CountryUnited Kingdom
CityEssex
Period8/09/1010/09/10

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