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Special Session
Special Session on
Text Mining
 - SSTM 2016

9- 11 November, 2016 - Porto, Portugal

Within the 8th International Conference on Knowledge Discovery and Information Retrieval - KDIR 2016


Ana Fred
Instituto de Telecomunicações / IST
Brief Bio
Ana Fred received the M.S. and Ph.D. degrees in Electrical and Computer Engineering, in 1989 and 1994, respectively, both from Instituto Superior Técnico (IST), Technical University of Lisbon, Portugal. She is a Faculty Member of IST since 1986, where she has been a professor with the Department of Electrical and Computer Engineering, and more recently with the Department of Biomedical Engineering. She is a researcher at the Pattern and Image Analysis Group of the Instituto de Telecomunicações. Her main research areas are on pattern recognition, both structural and statistical approaches, with application to data mining, learning systems, behavioral biometrics, and biomedical applications. She has done pioneering work on clustering, namely on cluster ensemble approaches. Recent work on biosensors hardware (including BITalino – and ECG-based biometrics (Vitalidi project) have been object of several nacional and internacional awards, as well as wide dissemination on international media, constituting a success story of knowledge transfer from research to market. She has published over 160 papers in international refereed conferences, peer reviewed journals, and book chapters.


With the increasing popularity and availability of Internet-based technologies, as well as the proliferation of digital computing devices and their use in communication, huge amounts of Human generated content is produced every day in the form of documents, email, instant messaging, social network sites, blogs, and other textual corpora. As a result, we have witnessed an increased demand for systems and algorithms capable of mining textual data, seeking interesting characteristics, hidden patterns, structure, trends, knowledge and key relationships within these large textual corpora. Text mining, combining the disciplines of data mining, information extraction, information retrieval, text categorization, probabilistic modeling, linear algebra, machine learning, and computational linguistics, is a new interdisciplinary field that emerged to address these issues. Examples of emergent applications include metadata generation, visualization techniques, information extraction, text segmentation and classification, text summarization, and trend analysis, to name a few.
This special session aims at sharing new ideas and works on models and approaches for improving over state of the art techniques for mining unstructured, semi-structured, and fully structured textual data.


Paper Submission: September 5, 2016 (expired)
Authors Notification: September 26, 2016
Camera Ready and Registration: October 4, 2016


Available soon.


Prospective authors are invited to submit papers in any of the topics listed above.
Instructions for preparing the manuscript (in Word and Latex formats) are available at: Paper Templates
Please also check the Guidelines.
Papers should be submitted electronically via the web-based submission system at:


After thorough reviewing by the special session program committee complemented by members of the main conference program committee, all accepted papers will be published in a special section of the conference proceedings book - under an ISBN reference and on CD-ROM support - and submitted for indexation by Thomson Reuters Conference Proceedings Citation Index (CPCI/ISI), INSPEC, DBLP, EI (Elsevier Engineering Village Index) and Scopus.
SCITEPRESS is a member of CrossRef ( and every paper is given a DOI (Digital Object Identifier).
All papers presented at the conference venue will be available at the SCITEPRESS Digital Library


KDIR Special Sessions - SSTM 2016