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Noah A. Smith
Researcher at University of Washington
Publications - 502
Citations - 40879
Noah A. Smith is an academic researcher from University of Washington. The author has contributed to research in topics: Parsing & Computer science. The author has an hindex of 93, co-authored 455 publications receiving 32507 citations. Previous affiliations of Noah A. Smith include Carnegie Mellon University & Brown University.
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Proceedings Article
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
TL;DR: This work connects measures of public opinion measured from polls with sentiment measured from text, and finds several surveys on consumer confidence and political opinion over the 2008 to 2009 period correlate to sentiment word frequencies in contemporaneous Twitter messages.
Proceedings ArticleDOI
Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
Suchin Gururangan,Ana Marasović,Ana Marasović,Swabha Swayamdipta,Kyle Lo,Iz Beltagy,Doug Downey,Noah A. Smith,Noah A. Smith +8 more
TL;DR: It is consistently found that multi-phase adaptive pretraining offers large gains in task performance, and it is shown that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable.
Proceedings ArticleDOI
Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments
Kevin Gimpel,Nathan Schneider,Brendan O'Connor,Dipanjan Das,Daniel Mills,Jacob Eisenstein,Michael Heilman,Dani Yogatama,Jeffrey Flanigan,Noah A. Smith +9 more
TL;DR: A tagset is developed, data is annotated, features are developed, and results nearing 90% accuracy are reported on the problem of part-of-speech tagging for English data from the popular micro-blogging service Twitter.
Proceedings Article
A Simple, Fast, and Effective Reparameterization of IBM Model 2
TL;DR: A simple log-linear reparameterization of IBM Model 2 that overcomes problems arising from Model 1’'s strong assumptions and Model 2’s overparameterization is presented.
Proceedings Article
Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters
TL;DR: This work systematically evaluates the use of large-scale unsupervised word clustering and new lexical features to improve tagging accuracy on Twitter and achieves state-of-the-art tagging results on both Twitter and IRC POS tagging tasks.