Y. Albert Park

Also published as: Albert Park


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Towards Automatically Classifying Depressive Symptoms from Twitter Data for Population Health
Danielle L. Mowery | Albert Park | Craig Bryan | Mike Conway
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)

Major depressive disorder, a debilitating and burdensome disease experienced by individuals worldwide, can be defined by several depressive symptoms (e.g., anhedonia (inability to feel pleasure), depressed mood, difficulty concentrating, etc.). Individuals often discuss their experiences with depression symptoms on public social media platforms like Twitter, providing a potentially useful data source for monitoring population-level mental health risk factors. In a step towards developing an automated method to estimate the prevalence of symptoms associated with major depressive disorder over time in the United States using Twitter, we developed classifiers for discerning whether a Twitter tweet represents no evidence of depression or evidence of depression. If there was evidence of depression, we then classified whether the tweet contained a depressive symptom and if so, which of three subtypes: depressed mood, disturbed sleep, or fatigue or loss of energy. We observed that the most accurate classifiers could predict classes with high-to-moderate F1-score performances for no evidence of depression (85), evidence of depression (52), and depressive symptoms (49). We report moderate F1-scores for depressive symptoms ranging from 75 (fatigue or loss of energy) to 43 (disturbed sleep) to 35 (depressed mood). Our work demonstrates baseline approaches for automatically encoding Twitter data with granular depressive symptoms associated with major depressive disorder.


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Bilingual Random Walk Models for Automated Grammar Correction of ESL Author-Produced Text
Randy West | Y. Albert Park | Roger Levy
Proceedings of the Sixth Workshop on Innovative Use of NLP for Building Educational Applications

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Automated Whole Sentence Grammar Correction Using a Noisy Channel Model
Y. Albert Park | Roger Levy
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies


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Minimal-length linearizations for mildly context-sensitive dependency trees
Y. Albert Park | Roger Levy
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics