We welcome original research papers that identify key problems related to noisy text analytics and offer solutions. Potential topics include (but 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
• Issues in keyword search in presence of noise in unstructured text data
• Text analysis techniques for analysis and mining of on-line communication texts such as transcribed calls, web logs, chat logs, tweets, 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
• Machine Translation of Noisy Text
  
• 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
• All other topics arising from noise and its effects on textual data
  

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Submission Guidelines

Selected papers from the workshop will be invited to submit expanded versions for publication in the International Journal of Document Analysis and Recognition published by Springer in a special issue.