We address the problem of part-of-speech tagging for English data from the popular microblogging service Twitter. We develop a tagset, annotate data, develop features, and report tagging results nearing 90% accuracy. The data and tools have been made available to the research community with the goal of enabling richer text analysis of Twitter and related social media data sets.
|Title of host publication||Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics|
|Publisher||Association for Computational Linguistics|
|Number of pages||6|
|Publication status||Published - 2011|