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Subject: [ E-CFP ] CFP -- text data learning at IDEAL (October 20-23, 2013)
From: <vogel_(on)_cs.tcd.ie>
Date received: 07 May 2013
Deadline: 24 May 2013
Start date: 20 Oct 2013







Special Session on Text Data Learning
http://www.scss.tcd.ie/IDEAL2013-TDL/ 

Call for Papers

The 14th International Conference on Intelligent Data Engineering and
Automated Learning (IDEAL'2013) Hefei, Anhui, China, October 20-23,
2013 http://nical.ustc.edu.cn/ideal13/

Overview

Tremendous efforts have been devoted to developing and applying
different machine learning technologies to natural language text data,
greatly expanding the fields of information retrieval and natural
language processing, creating new areas of research. However, many
challenges remain, such as:

    how we can successfully process different natural language related
    tasks with machine learning: ranking documents, classifying text,
    clustering, summarizing, analyzing, extracting information, and so
    on?

    how we can circumvent the barrier of lacking enough annotated
    data, despite the vast quantities of unannotated data?

    how we can adapt machine learning solutions across domains,
    genres, and languages?

    how we can make full use of the characteristics of text data in
    building machine learning based solutions?

    how we can create text learning systems to process Big Data in
    distributed and parallel environments?

This special session on text data learning will provide a forum for
researchers and practitioners interested in information retrieval and
natural language processing to exchange and report their latest
findings in applying machine learning to understanding and mining
natural language text data.

Topics of Interest

We invite researchers and practitioners to submit their original and
unpublished work on all aspects of computational approaches to text
data learning and their applications, including, but not limited to:

* Supervised, unsupervised and semi-supervised machine learning
  methods applied to managing, analyzing, understanding, mining, and
  exploiting text data in both normal and "big" scale

* Computational learning technologies adapted to processing text data
  across domain, genre, language, and scale

* Intelligent text data preparation, annotation and analysis for
  effectively learning

* Data representation for text learning and inference

* Novel applications of text data learning in Internet, social,
  enterprise and mobile environments

* Empirical and theoretical comparisons of text data learning methods
  including novel evaluation methods

We especially welcome submissions on learning methods considering the
special characteristics of text data, e.g. sequential, structural, and
graphical.

Submission


Please follow the IDEAL 2013 instructions for authors
(http://nical.ustc.edu.cn/ideal13/submission.html) to prepare and
submit your papers via the IDEAL 2013 online submission system
(https://www.easychair.org/account/signin.cgi?conf=ideal2013). Please
specify that your paper is for the Special Session on Text Data
Learning. All accepted papers will be included in the IDEAL 2013
Proceedings, which will be published by Springer Verlag in the Lecture
Notes on Computer Science Series, and indexed in EI and DBLP.


Important Dates

Paper Submission Deadline: 	24 	May 	2013
Notification of Acceptance:	5 	July 	2013
Camera-Ready Copy Due: 	26 	July 	2013
Early Registration: 	26 	July 	2013
Conference Presentation: 	20-23 	October 	2013

Organisers

Baoli Li, Henan University of Technology, China (csblli at gmail.com)
Carl Vogel, Trinity College Dublin, Ireland (vogel at tcd.ie)

PC Members

Khurshid Ahmad, Trinity College Dublin, Ireland

Walter Daelemans, University of Antwerp, Belgium

Jinhua Du, Xi'An University of Technology, China

Martin Emms, Trinity College Dublin, Ireland

Moshe Koppel, Bar-Ilan University, Israel

Qin Lu, The Hong Kong Polytechnic University, Hong Kong

Saturnino Luz, Trinity College Dublin, Ireland

Xueqiang Lv, Beijing Information Science and Technology University, China

Erwan Moreau, Trinity College Dublin, Ireland

Brian Murphy, Carnegie Mellon University, USA

Saurav Sahay, Intel Labs, USA

Zhifang Sui, Peking University, China

Andreas Vlachos, University of Cambridge, UK

Dong Zhou, Hunan Univesity of Science and Technology, China


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