Natural Language Generation (NLG) is defined as the systematic approach for producing human understandable natural language text based on non-textual data or from meaning representations. This is a significant area which empowers human-computer interaction. It has also given rise to a variety of theoretical as well as empirical approaches. This paper intends to provide a detailed overview and a classification of the state-of-the-art approaches in Natural Language Generation. The paper explores NLG architectures and tasks classed under document planning, micro-planning and surface realization modules. Additionally, this paper also identifies the gaps existing in the NLG research which require further work in order to make NLG a widely usable technology.
Publié le : 2017-05-12
Classification:
Artificial Intelligence; Natural Language Processing,
Natural language processing, document planning, micro-planning, surface realization,
68T50, 03B65
@article{cai2017_1_1,
author = {Rivindu Perera; Auckland University of Technology and Parma Nand; Auckland University of Technology},
title = {Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature},
journal = {Computing and Informatics},
volume = {35},
number = {4},
year = {2017},
language = {en},
url = {http://dml.mathdoc.fr/item/cai2017_1_1}
}
Rivindu Perera; Auckland University of Technology; Parma Nand; Auckland University of Technology. Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature. Computing and Informatics, Tome 35 (2017) no. 4, . http://gdmltest.u-ga.fr/item/cai2017_1_1/