The recent years have witnessed an unprecedented growth in the volume of unstructured data expressed in natural language. This explosion in the volume of text can to a large extent be attributed to the emergence of Web 2.0 platforms and online communities, including blogs, forums, review sites, and social networks. Hidden within these novel channels is valuable information, which, if properly identified and extracted, can be employed to enhance a plethora of corporate activities. For example, product reviews written by consumers on blogs or retailer websites contain useful information about brand perception and product quality.
The scientific discipline of Natural Language Processing (or Computational Linguistics) and the applied field of Text Analytics, in which Natural Language Processing coalesces with Machine Learning and Data Mining, have also evolved over the years to keep pace with the proliferation of novel sources of text data. Natural Language Processing (NLP) and Text Analytics (TA) algorithms and techniques are increasingly being developed, adopted and deployed for addressing a wide spectrum of real-life, industrial problems. Typical examples include Document Classification, Document Clustering, Topic Detection and Modelling, and Opinion Mining/Sentiment Analysis. However, at the same time, the successful application of such innovative technologies raises a number of pertinent research questions, with both theoretical and practical ramifications.
Ashwin Ittoo, HEC Management School, University of Liege, Belgium, email@example.com
Antal van den Bosch, Centre for Language Studies, Radboud University Nijmegen, Nijmegen, The Netherlands, firstname.lastname@example.org
Nguyen Le Minh, School of Information Science, Japan Advanced Institute of Science and Technology, Japan, email@example.com
The main objective of this Special Issue is to collect and consolidate high-quality knowledge on state of the art research in NLP/TA methods and their applications in industrial (enterprise) activities.
Authors are encouraged to submit articles describing original, innovative, unpublished and recent research work dealing with NLP/TA methods in industrial applications, architectures or frameworks for industrial NLP/TA systems, NLP/TA resources, and any other aspects and challenges pertaining to the development, adoption and deployment of NLP/TA methods in industrial contexts.
Topics of interest for this special issue are the following:
* Social Media and Web Data: Information extraction (from social media); Sentiment Analysis and Opinion Mining; Topic Detection and Modelling; Robust NLP/TA for ill-formed texts;
* QA (Question-Answering) Systems: Community-Based QA; Natural Language interfaces to Databases; QA systems design and development; Multi-Lingual QA; Geographical QA; Non-factoid QA; Domain-Specific QA;
* Information Extraction (IE): Term Extraction; Semantic Relationship Extraction; Event Detection and Extraction; Named Entity Recognition; IE from Novel Sources (tweets, dialogues/conversations, log files, medical records);
* General Text Analytics: Text Classification; Text Clustering; Topic Detection and Modelling; Sentiment Analysis and Opinion Mining; Text Summarization; Plagiarism Detection; Fake Reviews Detection;
* NLP/TA for Big Data: Techniques for Mining Very Large and/or Streaming Text Corpora; Real-Time Analytics; Architectures and Frameworks;
* Semantic Web and Linked Data: Ontology Learning; Ontology Alignment; Ontology Management; LinkedData and OpenLinkedData in Enterprises; Web 2.0 Knowledge Management (enterprise Taxonomies, Folksonomies); Entity Linking;
* Natural Language in Conceptual/Ontological Modeling: Analysis of Natural Language Descriptions; Terminological Ontologies; Consistency Checking; Metadata Creation and Harvesting; Ontology- driven Systems Integration;
* Enterprise Systems: Enterprise Application Integration; NLP/TA for Enterprise Systems (ERP, CRM).
SUBMISSION PROCEDURE AND TIMELINE
1. Authors are requested to email an extended abstract of a maximum of 1000 words to Ashwin Ittoo at firstname.lastname@example.org.
a. The extended abstract should clearly illustrate the problem being solved and its pertinence, the methodology adopted, and results, if available.
b. Images, tables and the reference list can be put in appendices.
c. The email subject should be ìCOMIND SIî
d. The deadline for sending the abstract is 1 May 2014.
2. Authors will be informed by email if their abstracts are accepted or not on 2 June 2014.
3. Accepted authors are required to submit their full papers via the journal?s submission system, following the formatting guidelines offered by the publisher; submissions via email or any other means are not allowed. The deadline for full papers is 1 December 2014.
4. Authors will be informed of the results of the first round of revision by 16 February 2015.
5. Selected authors may have to re-submit a revised version of their articles for revision by 31 March 2015.
6. Authors will be informed of the final decisions by 1 June 2015.
ABOUT COMPUTERS IN INDUSTRY
Computers in Industry is an Elsevier Science Journal (5 year Impact Factor: 2.062), which publishes high-quality, original research papers that
* Show new trends in and options for the use of?Information and Communication Technology (ICT)?in industry.
* Link or integrate different technology fields in the broad area of?computer applications?for?industry.
* Link or integrate different application areas of?ICT?in industry.
For the full aims and scope, please visit http://www.journals.elsevier.