24.2144, Calls: Computational Linguistics, Text/Corpus Linguistics/China

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Tue May 21 17:04:14 UTC 2013

LINGUIST List: Vol-24-2144. Tue May 21 2013. ISSN: 1069 - 4875.

Subject: 24.2144, Calls: Computational Linguistics, Text/Corpus Linguistics/China

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Editor for this issue: Alison Zaharee <alison at linguistlist.org>

Date: Tue, 21 May 2013 13:03:24
From: Carl Vogel [vogel at tcd.ie]
Subject: Special Session on Text Data Learning: 14th IDEAL

E-mail this message to a friend:
Full Title: Special Session on Text Data Learning: 14th IDEAL 
Short Title: IDEAL2013-TDL 

Date: 20-Oct-2013 - 23-Oct-2013
Location: Hefei, Anhui, China 
Contact Person: Baoli Li
Meeting Email: csblli at gmail.com
Web Site: http://www.scss.tcd.ie/IDEAL2013-TDL 

Linguistic Field(s): Computational Linguistics; Text/Corpus Linguistics 

Call Deadline: 31-May-2013 

Meeting Description:

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

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.

Call for Papers:

We invite researchers and practitioners to submit their original and unpublished work on all aspects of computational approaches to text data learning and their applications, including, but not limited to: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


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.


Baoli Li, Henan University of Technology, China
Carl Vogel, Trinity College Dublin, Ireland

Programme Committee:

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
John Nerbonne, University of Groningen, The Netherlands
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

LINGUIST List: Vol-24-2144	

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