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- John Blitzer, Mark Dredze, Fernando Pereira
- ACL
- 2007
Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed differently in different domains, and annotating corpora for every… (More)
- Koby Crammer, Alex Kulesza, Mark Dredze
- Machine Learning
- 2009
We present AROW, an online learning algorithm for binary and multiclass problems that combines large margin training, confidence weighting, and the capacity to handle non-separable data. AROW… (More)
- Mark Dredze, Koby Crammer, Fernando Pereira
- ICML
- 2008
We introduce confidence-weighted linear classifiers, which add parameter confidence information to linear classifiers. Online learners in this setting update both classifier parameters and the… (More)
- Michael J. Paul, Mark Dredze
- ICWSM
- 2011
Analyzing user messages in social media can measure different population characteristics, including public health measures. For example, recent work has correlated Twitter messages with influenza… (More)
The ubiquity of social media provides a rich opportunity to enhance the data available to mental health clinicians and researchers, enabling a better-informed and better-equipped mental health field.… (More)
- Timothy W. Finin, William Murnane, Anand Karandikar, Nicholas Keller, Justin Martineau, Mark Dredze
- Mturk@HLT-NAACL
- 2010
We describe our experience using both Amazon Mechanical Turk (MTurk) and CrowdFlower to collect simple named entity annotations for Twitter status updates. Unlike most genres that have traditionally… (More)
- Mark Dredze, Paul McNamee, Delip Rao, Adam Gerber, Timothy W. Finin
- COLING
- 2010
The integration of facts derived from information extraction systems into existing knowledge bases requires a system to disambiguate entity mentions in the text. This is challenging due to issues… (More)
- Glen Coppersmith, C. M. Keith Harman, Mark Dredze
- ICWSM
- 2014
Traditional mental health studies rely on information primarily collected through personal contact with a health care professional. Recent work has shown the utility of social media data for studying… (More)
From ADHD to SAD: Analyzing the Language of Mental Health on Twitter through Self-Reported Diagnoses
- Glen Coppersmith, Mark Dredze, C. M. Keith Harman, Kristy Hollingshead
- CLPsych@HLT-NAACL
- 2015
Many significant challenges exist for the mental health field, but one in particular is a lack of data available to guide research. Language provides a natural lens for studying mental health – much… (More)
- Koby Crammer, Mark Dredze, Fernando Pereira
- NIPS
- 2008
Confidence-weighted (CW) learning [6], an online learning method for linear classifiers, maintains a Gaussian distributions over weight vectors, with a covariance matrix that represents uncertainty… (More)