Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature

Authors

  • Rivindu Perera Auckland University of Technology, New Zealand
  • Parma Nand Auckland University of Technology, New Zealand

DOI:

https://doi.org/10.4149/cai_2017_1_1

Keywords:

Natural language processing, document planning, micro-planning, surface realization

Abstract

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.

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Author Biographies

Rivindu Perera, Auckland University of Technology, New Zealand

Researcher in Natural Language Processing. PhD candidate in Auckland University of Technology.

Parma Nand, Auckland University of Technology, New Zealand

Program leader BCIS

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Published

2017-05-09

How to Cite

Perera, R., & Nand, P. (2017). Recent Advances in Natural Language Generation: A Survey and Classification of the Empirical Literature. Computing and Informatics, 36(1), 1–32. https://doi.org/10.4149/cai_2017_1_1