Moving from data to text using causal statements in explanatory narratives

Donald Ewan Matheson; Gowri Somayajulu Sripada; George MacLeod Coghill

doi:10.1109/UKCI.2010.5625591

Moving from data to text using causal statements in explanatory narratives

Donald Ewan Matheson, Gowri Somayajulu Sripada, George MacLeod Coghill

Research output: Chapter in Book/Report/Conference proceeding › Published conference 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 language	English
Title of host publication	Proceedings of the 10th UK Workshop on Computational Intelligence (UKCI)
Publisher	IEEE Explore
Number of pages	6
ISBN (Electronic)	978-1-4244-8775-2, 978-1-4244-8773-8
ISBN (Print)	978-1-4244-8774-5
DOIs	https://doi.org/10.1109/UKCI.2010.5625591
Publication status	Published - 2010
Event	Proceedings of the 10th UK Workshop on Computational Intelligence (UKCI) - Essex, United Kingdom Duration: 8 Sep 2010 → 10 Sep 2010

Publication series

Name	UK Workshop on Computational Intelligence (UKCI)
Publisher	IEEE
ISSN (Print)	2162-7657

Conference

Conference	Proceedings of the 10th UK Workshop on Computational Intelligence (UKCI)
Country/Territory	United Kingdom
City	Essex
Period	8/09/10 → 10/09/10

Access to Document

10.1109/UKCI.2010.5625591Licence: Unspecified

Cite this

Moving from data to text using causal statements in explanatory narratives. / Matheson, Donald Ewan; Sripada, Gowri Somayajulu ; Coghill, George MacLeod.

Proceedings of the 10th UK Workshop on Computational Intelligence (UKCI). IEEE Explore, 2010. (UK Workshop on Computational Intelligence (UKCI)).

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

Matheson, DE, Sripada, GS & Coghill, GM 2010, Moving from data to text using causal statements in explanatory narratives. in Proceedings of the 10th UK Workshop on Computational Intelligence (UKCI). UK Workshop on Computational Intelligence (UKCI), IEEE Explore, Proceedings of the 10th UK Workshop on Computational Intelligence (UKCI), Essex, United Kingdom, 8/09/10. https://doi.org/10.1109/UKCI.2010.5625591

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title = "Moving from data to text using causal statements in explanatory narratives",

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.",

author = "Matheson, {Donald Ewan} and Sripada, {Gowri Somayajulu} and Coghill, {George MacLeod}",

year = "2010",

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language = "English",

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N2 - 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.

AB - 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.

UR - https://www.researchgate.net/publication/238518586_Moving_from_data_to_text_using_causal_statements_in_explanatory_narratives

U2 - 10.1109/UKCI.2010.5625591

DO - 10.1109/UKCI.2010.5625591

M3 - Published conference contribution

SN - 978-1-4244-8774-5

T3 - UK Workshop on Computational Intelligence (UKCI)

BT - Proceedings of the 10th UK Workshop on Computational Intelligence (UKCI)

PB - IEEE Explore

T2 - Proceedings of the 10th UK Workshop on Computational Intelligence (UKCI)

Y2 - 8 September 2010 through 10 September 2010

ER -

Moving from data to text using causal statements in explanatory narratives

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