AND 2008

AND 2008
2nd Workshop on Analytics for Noisy Unstructured Text Data
Dates	Jul 24, 2008 (iCal) - Jul 24, 2008
Homepage:	and2008workshop.googlepages.com
Location
Location:	Singapore, Singapore
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Important dates
Submissions:	May 16, 2008
Notification:	Jun 6, 2008
Table of Contents

SIGIR-08 Workshop 

2nd Workshop on Analytics for Noisy Unstructured Text Data 
24 July 2008 , Singapore

http://and2008workshop.googlepages.com/

Call for Papers 

Workshop Description and Objectives 
Noise is an unavoidable fact of life. It can manifest itself at the earliest stages of processing in the form of degraded inputs that our systems must be prepared to handle. People are adept when it comes to pattern recognition tasks involving typeset or handwritten documents or recorded speech, machines less-so. From the perspective of down-stream processes that take as their inputs the outputs of recognition systems, including document analysis and OCR, noise can be viewed as the errors made by earlier stages of processing, which are rarely perfect and sometimes quite brittle.   
Noisy unstructured text data is also found in informal settings such as online chat, SMS, email, message board and newsgroup postings, blogs, wikis and web pages. In addition to the aforementioned recognition errors, such text may contain spelling errors, abbreviations, non-standard terminology, missing punctuation, misleading case information, as well as false starts, repetitions, and pause-filling sounds such as "um" and "uh" in the case of speech. 
By its very nature, noisy text warrants moving beyond traditional text analytics techniques. Noise introduces challenges that need special handling, either through new methods or improved versions of existing ones. We invite you to submit your own unique perspectives on this important topic.

Topics of Interest (not limited to) 
Information Retrieval and Information Extraction on noisy texts 
IR-related tasks (classification, clustering, genre recognition, document summarization, keyword search) on noisy texts 
Formal models for noise, characterization and classification of noise 
Treatment of noisy data in special application fields
        - Historical Texts 
       - Multilingual Texts 
       - Blogs 
       - Chat logs/SMS 
       - Social Network Analysis 
       - Patent Search 
       - Optical Character Recognition 
       - Automated Speech Recognition 
       - Machine Translation 
Data sets, benchmarks and evaluation techniques for analysis of noisy texts

Participation 
We hope that the workshop will allow researchers working in areas related to unstructured data analytics, Natural Language Processing, Information Extraction, Information Retrieval, etc., to focus on the needs of users extracting useful information from noisy text. The target audience is a mixture of academia and industry researchers working with noisy text. We believe this work is of direct relevance to domains such as call centers, the world-wide web, and government organizations that need to analyze huge amounts of noisy data. 

Important Dates 
Paper Submission: May 16th, 2008 
Notification of Acceptance: Jun 6th, 2008 
Camera-Ready papers due: Jun 20th, 2008 
Workshop at SIGIR 2008: Jul 24th, 2008
  
Submission Requirements 
We invite papers up to 8 pages in length in the style specified at http://and2008workshop.googlepages.com/submission There will also be a Best Student Paper Award. Papers with a student as the primary author/presenter will be eligible for this award.   

Publication 
We are currently in negotiation with a leading publisher for the proceedings to be available onsite. We have also received tentative approval for a special issue of a journal for post-workshop publication of selected papers. 

Workshop Chairs  
Daniel Lopresti 
Lehigh University 
Shourya Roy 
IBM Research, India Research Lab 
 Klaus U Schulz 
University of Munich
L. Venkata Subramaniam 
IBM Research, India Research Lab 

Workshop contacts 
* L. V. Subramaniam lvsubram@in.ibm.com 
* Shourya Roy rshourya@in.ibm.com 
	

This CfP was obtained from WikiCFP