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October 13, 2016

Challenges for Data-driven Natural Language Analysis beyond Standard Data

Time
October 13 – 14, 2016

Recent advances in empirical natural language processing and machine learning have coincided with the wide availability of new resources, such as social media data, learner or code-switching data, historical data, speech data, among many others. While most traditional tasks in natural language processing rely on carefully curated and linguistically annotated data, this newer generation of "non-standard" data is often noiser and resists traditional assumptions about language use and data annotation. The goal of this workshop is to bring together researchers working on non-standard data from a variety of angles, ranging from issues about annotation standards and methodolody to linguistic issues and issues in machine learning and experimentation. 

 
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