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HAI Weekly Seminar with Andrew Schwartz - Modeling the People Behind the Language: Human-Centered Natural Language Processing

Event Details

Friday, April 24, 2020
11:00 a.m. - 12:00 p.m. PDT

Event Type

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Biography: H. Andrew Schwartz is director of the Human Language Analysis Beings (HLAB) and Assistant Professor of Computer Science at Stony Brook University (SUNY). His interdisciplinary research focuses on developing large-scale language analyses for health and social sciences as well as integrating human factors into predictive models to improve natural language processing for diverse populations. He is an active member of many subcommunities of AI (e.g. NAACL, EMNLP, WWW), Psychology (e.g. Psychological Science, JPSP, PNAS), and health informatics (e.g. JMIR, Nature - Sci Reports, Am Jour. of Public Health). He was previously Lead Research Scientist for the World Well-Being Project at the University of Pennsylvania, an interdisciplinary team studying how big language analyses can reveal and predict differences in health, personality, and well-being. He received his PhD in Computer Science from the University of Central Florida in 2011 with research on acquiring lexical semantic knowledge from the Web.

Abstract: Natural Language Processing (NLP) conventionally focuses on modeling words, phrases, or documents. However, natural language is generated by people and with the growth of social media and automated assistants, NLP is increasingly tackling human problems that are social, psychological, or medical in nature. Language shared on Facebook and Twitter has already been used to measure characteristics from individual depression and personality to community well-being, mortality, and, recently, COVID symptom rates. In this talk I will summarize recent work from the Human Language Analysis Lab to further NLP towards modeling people as the originators of language. This includes controlling for and correcting biases from extralinguistic variables (demographics, socioeconomics), placing language in time (forecasting future outcomes), and leveraging the inherent multi-level structure (people, who belong to communities, generate language). Taken together, I will suggest that considering the people behind the language not only offers opportunities for improved accuracies but it could be fundamental to NLP's role in our increasingly digital world.