Challenges for Data-driven Natural Language Analysis beyond Standard Data

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.