Special Issue on

"Natural Language Generation with Computational Intelligence"


With an impact factor of 3.647 (June 2016), the IEEE Computational Intelligence Magazine (IEEE CIM) is an influential media for publishing high-quality peer-reviewed research. CIM publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE CIS). Selection of papers for the magazine is extremely competitive. Previous special issues had an acceptance rate as minimum as 6% of submitted papers.

Aims and Scope

Computational Intelligence (CI) encompasses the theory, design, application, and development of biologically and linguistically motivated computational paradigms emphasizing neural networks, connectionist systems, genetic algorithms, evolutionary programming, fuzzy systems, and hybrid intelligent systems in which these paradigms are contained. These techniques and their hybridizations work in a cooperative way, taking profit from the main advantages of each individual technique, in order to solve complex real-world problems for which other techniques are not well suited.

Natural Language Generation (NLG) approaches, which build and communicate information expressed in terms of natural language, have emerged as feasible complements which, while still exploiting the full potential of standard Data Science analytics, allow for a better understanding of what underlies in such data. In this regard, recent studies show that non-specialized users actually strongly demand textual descriptions of data as a necessary and complementary means for better understanding of graphics and visualizations. Within this context, CI and, more specifically, Soft Computing capability for representing and dealing with imprecision and uncertainty is potentially an adequate tool for playing a relevant role in some key tasks in the usual NLG pipeline, such as content determination, lexicalization, referring expressions generation, linguistic realization, etc.

On the one hand, Data Science has traditionally relied on analytics and visualization techniques to make sense of large volumes of data. Data scientists employ different techniques such as statistics, signal processing, pattern recognition, data mining or machine learning among others to extract relevant information from such amounts of data. However, communication of the extracted information after the analytics process is usually made through tables, graphics or visualization techniques, which in general demand interpretation efforts from the user side and sometimes require a rather extensive academic development or expertise for its actual comprehension.

On the other hand, text summaries in natural language can be an effective way of presenting data in practical applications. Of course, an enormous practical advantage of graphical presentations of data is that they can be produced automatically, whereas textual narratives, notes or summaries derived from data usually need to be written by a person (and hence are expensive). Nevertheless, if good-quality textual summaries could be produced automatically (and hence cheaply and quickly), then they would be much more attractive for practitioners.
It is worthy to remark that systems that summarize texts generally use natural language processing (NLP) techniques along with information retrieval techniques to identify key phrases and sentences, and then stitch these together into a coherent summary using limited NLG. Moreover, CI techniques usually applied to summarize textual inputs are quite different from those techniques used to describe or summarize data which is the main target in this special issue. Therefore, pure NLP techniques as well as NLG and CI techniques for text summarization are out of the scope of this special issue.

This proposal of IEEE CIM Special Issue will be based on an open Call for Papers aimed at collecting as many high quality contributions as possible with the aim of selecting among them the best ones to be published in the special issue. Our view is that it is timely to edit a special issue related to the current state-of-the-art, recent trends and challenges in the research field of NLG with CI in a reputed journal as IEEE CIM.

Topics of Interest include (but are not limited to)

  • Data-to-Text Systems combining Natural Language Generation (NLG) and Computational Intelligence (CI) methods
  • CI based Methods applied to NLG Process: Content Determination, Lexicalization, Referring Expressions Generation, Linguistic Realization, etc.
  • Dealing with Imprecision and Uncertainty in NLG
  • Machine and Deep Learning applied to NLG
  • CI based Experimental and Empirical Methods for Validation, Corpus Building, etc.
  • NLG with CI Integrating Paradigms: Semantic Technologies, Linked/Open Data, Big Data, etc.
  • Real NLG Systems with CI for: Dialogue Systems, Sentiment Analysis, Affective Computing, Smart Environments, Natural Language Human-Computer Interfaces, Augmented and Virtual Reality, Human-Robot Interaction, etc.

Submission Process

The IEEE CIM requires all prospective authors to submit their manuscripts in electronic format, as a PDF file. The maximum length for Papers is typically 20 double-spaced typed pages with 12 point font, including figures and references. Submitted manuscript must be typewritten in English in single-column format. Authors of Papers should specify on the first page of their submitted manuscript up to 5 keywords. Additional information about submission guidelines and information for authors is provided at the IEEE CIM website.

Submission will be made via https://easychair.org/conferences/?conf=ieeecimcinlg2017.

The special issue is expected to include around 3-4 high quality papers.

Important Dates

  • 10th September, 2016: Submission of Manuscripts
  • 15th November, 2016: Notification of Review Results
  • 30th December, 2016: Submission of Revised Manuscripts
  • 15th February, 2017: Notification of Review Results
  • 15th March, 2017: Submission of Final Manuscripts
  • Publication: August 2017

Guest Editors

Dr. José María Alonso

IEEE-CIM Associate Editor

University of Santiago de Compostela

Research Centre in Information Technologies
(Centro Singular de Investigación en Tecnoloxías da Información, CiTIUS)
Campus Vida, E-15782, Santiago de Compostela, Spain

Website · Google Scholar · E-mail

Professor Alberto Bugarín

University of Santiago de Compostela

Research Centre in Information Technologies
(Centro Singular de Investigación en Tecnoloxías da Información, CiTIUS)
Campus Vida, E-15782, Santiago de Compostela, Spain

Website · Google Scholar · E-mail

Professor Ehud Reiter

University of Aberdeen

Department of Computing Science
Aberdeen, United Kingdom

Website · Google Scholar · E-mail

Guest Editors Short-Bio

Dr. José María Alonso received his M.S. and Ph.D. degrees in Telecommunication Engineering, both from the Technical University of Madrid (UPM), Spain, in 2003 and 2007, respectively. Since June 2016, he is postdoctoral researcher at the University of Santiago de Compostela, in the Research Centre in Information Technologies (Centro Singular de Investigación en Tecnoloxías da Información, CiTIUS). He is currently secretary of the European Society for Fuzzy Logic and Technology (EUSFLAT), Vice-chair of the Task Force on “Fuzzy Systems Software” in the Fuzzy Systems Technical Committee of the IEEE Computational Intelligence Society, and Associate Editor of the IEEE Computational Intelligence Magazine (ISSN:1556-603X). He has published more than 85 papers in international journals, book chapters and in peer-review conferences. According to Google Scholar (accessed on June 29, 2016) he has h-index=14 and i10-index=24. His research interests include computational intelligence, natural language generation, sensory analysis, development of free software tools, fuzzy modeling for control and classification problems, assessing interpretability of fuzzy systems, knowledge extraction and representation, integration of expert and induced knowledge, sensory analysis, advance multi-sensor fusion, WiFi localization, and autonomous robotic navigation in complex environments. He has already acted as guest editor four times. He co-edited a special issue on “Interpretable Fuzzy Systems” in Information Science (Vol. 181:20, 2011); a special issue on “Software Tools for Soft Computing” in the International Journal of Computational Intelligence Systems (Vol. 6:sup1, 2013); a special issue on “Computational Intelligence Software” in the IEEE Computational Intelligence Magazine (Vol. 11, Number 2, 2016); and a virtual special issue on “Selected Papers from IFSA-EUSFLAT2015 Conference” in the International Journal of Approximate Reasoning (Vol. 74, 2016).

Professor Alberto Bugarín is Full Professor with the University of Santiago de Compostela, Spain. He has participated in 55 R+D+i projects, contracts with companies and other research activities in areas such as fuzzy rule-based systems, machine learning for prediction models (scalable to Big Data) and descriptive data mining (Data To Text systems) using fuzzy techniques. He was the principal investigator in 14 of these activities. His research has been applied in areas such as automatic generation of linguistic descriptions of data in natural language, improvement of industrial production processes, documental information retrieval, e-learning and intelligent monitoring in medical and industrial domains. Prof. Bugarín research is described in almost 200 scientific papers in the areas mentioned above. According to Google Scholar (accessed on June 29, 2016) he has h-index=15 and i10-index=30. He is a member of the Board of the Spanish Association for Artificial Intelligence (CAEPIA) and has been member of the editorial board, program and organizing committee of 26 journals and international conferences.

Professor Ehud Reiter is Full Professor with the University of Aberdeen, Scotland. His education (BA, MA, PhD) was all at Harvard University (USA). He has both commercial and academic background. He has been a lecturer in the Aberdeen Computing Science Department since August 1995. He founded the Natural Language Generation (NLG) research group at Aberdeen. Before that he was a Senior Scientist in CoGenTex, a small (USA) NLP software house; a Research Fellow at the University of Edinburgh (UK); and a Research Associate at the University College of Cork (Ireland). He is currently Chief Scientist of Arria NLG company. His research interests include natural-language generation, data-to-text, medical informatics, multimodal systems, etc. He is world-wide recognized as pioneer in the NLG research field. The book entitled “Building natural language generation systems” (co-authored by E. Reiter and R. Dale) is considered as an essential reference for all readers in the field. According to Google Scholar (accessed on June 29, 2016) he has h-index=37 and i10-index=89.