Philip Resnik - "From Linguistic Signal to Mental State: Computational Models of Framing"

Philip Resnik is a professor at University of Maryland in the Department of Linguistics and Institute for Advanced Computer Studies. He does research in computational linguistics, with interests both in the application of natural language processing techniques to practical problems, and in the modeling of human linguistic processes. He has worked in a wide range of research areas, including core NLP areas like word sense disambiguation, applications such as cross-language information retrieval and machine translation, and work on the science of language including lexical semantics and computational psycholinguistics. Across the range of his work, he has been a proponent of finding the right balance between learning automatically through large scale data analysis, and incorporating linguistic and expert knowledge into our systems and models.  Resnik's most recent research focus has been in computational social science, with an emphasis on connecting the signal available in people's language use with underlying mental state -- this has applications in computational political science, particularly in connection with ideology and framing, and in mental health, focusing on the ways that linguistic behavior may help to identify and monitor depression, suicidality, and schizophrenia.  Outside his academic research, Resnik has been a technical co-founder of CodeRyte (NLP for electronic health records, acquired by 3M in 2012), and is an advisor to Converseon (social strategy and analytics), FiscalNote (machine learning and analytics for government relations), and SoloSegment (web site search and content optimization).

Abstract: According to one classic definition, framing is the use of language to "select some aspects of a perceived reality and make them more salient in a communicating text". It's a familiar notion in political science -- when politicians do it deliberately and egregiously, we call it "spin" -- but I would argue that it is also a fundamental property of linguistic communication in any setting. In this talk I'll discuss work on computational modeling of framing as a way of studying the connection between signals in language use and underlying mental state. This has applications not only in political science, where underlying mental state includes notions like party self-identification or ideological bias, but in mental health, where underlying mental state can include conditions such as schizophrenia or suicidality.