AND: Analytics for Noisy Unstructured Text Data

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Past:   Proceedings on DBLP

Future:  Post a CFP for 2011 or later   |   Invite the Organizers Email

 
 

All CFPs on WikiCFP

Event When Where Deadline
AND 2010 4th Workshop on Analytics for Noisy Unstructured Text Data
Oct 26, 2010 - Oct 26, 2010 Toronto, Canada Jul 20, 2010 (Jul 16, 2010)
AND 2009 Third Workshop on Analytics for Noisy Unstructured Text Data
Jul 23, 2009 - Jul 24, 2009 Barcelona Apr 20, 2009 (Jun 1, 2009)
AND 2008 2nd Workshop on Analytics for Noisy Unstructured Text Data
Jul 24, 2008 - Jul 24, 2008 Singapore May 16, 2008
 
 

Present CFP : 2010

Noisy unstructured text data is ubiquitous and abundant in real-world situations. Handling noisy text poses new challenges for Information Extraction (IE), Natural Language Processing (NLP), Information Retrieval (IR) and Knowledge Management (KM). Special handling of noise as well as noise-robust IR and KM techniques are essential to overcome these challenges. As in the case of AND 07, 08 and 09, we intend that AND 2010 will provide researchers an opportunity to present their latest results toward addressing these challenges. We seek papers dealing with all aspects of noisy unstructured text data and its processing.

Topics of Interest (not limited to)
• Methods for detecting and correcting errors in noisy text,
• Information Retrieval from noisy text data,
• Machine learning techniques for information extraction from noisy text,
• Rule-based approaches for handling noisy text
• Social network analysis involving noisy data
• Crowd-sourcing methods for dealing with noisy data
• Knowledge Management of noisy text data,
• Automatic classification and clustering of noisy unstructured text data,
• Noise-invariant document summarization techniques,
• Text analysis techniques for analysis and mining of on-line communication texts such as
transcribed calls, web logs, chat logs, tweets, microblogs, facebook posts, and email exchanges,
• Business Intelligence (BI) applications dealing with noisy text data,
• Document Representation and Content Analysis of noisy text documents
• Interplay between linguistic complexity and uncertainty characterizing noisy text data in downstream applications,
• Formal theory on characterization of noise,
• Genre recognition based on the type of noise,
• Characterizing, modeling and accounting for historical language change,
• Surveys relating to noisy text analytics
 

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