Philip Resnik
Philip Resnik
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A primary application area for these ideas involves applying computational models to the identification of linguistic signal related to mental health.
See my page about research and social impact for discussion. Current work includes collaborating with a colleague in the medical school to apply similar ideas to depression and schizophrenia; you, dear reader, are invited to contribute data to this effort here. I'm also very active in research on the application of computational linguistics methods in suicide prevention, including creation of the The University of Maryland Reddit Suicidality Dataset, as well as work addressing (un)availability of data for mental health research via The Mental Health Data Enclave, a joint project with NORC at the University of Chicago sponsored in part by an Amazon AWS Machine Learning Research Award.
The other main area in which I'm applying these ideas is computational political science -- again see discussion on my page about research and social impact, and also take a look at some of my previous research.
Outside academia, I've been involved in advising and spearheading development of the sentiment
analysis platform for Converseon Inc., a leading
social media analysis firm (which recently closed a $5M Series A); serving as an advisor to FiscalNote, provider of a real-time legal analytics platform that tracks, analyzes, and forecasts legal and policy data; and founding React Labs to commercialize my work on mobile real-time responses for improved measurement and engagement.
Computational psycholinguistics and neurolinguistics.
During the past several years, I have been re-engaging more fully with my
longstanding interests in computational psycholinguistics. I'm particularly
interested in the interactions between syntactically mediated compositional processes
and broader context, for which vector space representations (yes, including "deep learning", see below)
offer some interesting modeling tools. Some recent papers related to this line of work include
Ettinger, Phillips, and Resnik, Modeling N400 amplitude using vector space models of word representation (CogSci 2016) and
Ettinger, Resnik, and Carpuat, Retrofitting sense-specific word vectors using parallel text (NAACL 2016). I also remain quite interested in the possibility that ideas from (statistical)
information theory may have a useful role to play in explaining why
language works the way it does. (This is an idea I first began
exploring in my dissertation [ps,
pdf], back in 1993, and in
following years a variety of people like John Hale, Roger Levy, and
Florian Jaeger, among others, have done very interesting work in the
same spirit.) My psycholinguistics interests have led to interesting collaboration with my colleague Colin Phillips; I'm also interested in using Bayesian modeling as a way
to bring linguists here with cognitive modeling interests together
with computational linguists focusing on applications; folks who are interested
in that line of thinking should be talking with Naomi Feldman.
More recently, during my 2018-2019 sabbatical, I began getting up to speed on interests in computational cognitive neuroscience. As of Fall 2019 I'll be working with postdoc Shohini Bhattasali on applying computational models to neuroimaging data in order to better understand the physical basis of language comprehension and contextual influences on language (mis)understanding, in the context
of a MURI project involving document understanding, and I've also begun collaborating with Christian Brodbeck (along with Ellen Lau and Jonathan Simon) on neural representations of continuous speech and linguistic context.
Deep learning. I debated whether to include this here, because frankly I believe there is a ton of hype and many people are excited about so-called "deep learning" (a better term coined by Noah Smith is squash networks) for the wrong reasons. That said, it's practically impossible to get away from this topic, and I'm supervising students who do things with neural network models, so let me say what I do find interesting about this line of work. First, there is a great deal of power in representation learning (unsupervised feature learning) and the sharing of statistical power using subsymbolic representations to improve generalization. Second, deep learning is creating a renewed energy around fundamental scientific questions in computational linguistics that I care about, including the nature of lexical representations, compositional interpretation, and computational models of human sentence processing (see discussion of psycho- and neurolinguistics above). Third... Actually, that's about all that comes immediately to mind. :) If you're a prospective student and these approaches interest you, be prepared to show me that you've thought about why they should interest us!
Clinical informatics. Since about 1999 I've been involved in natural language
processing for clinical documentation. I helped start up
CodeRyte, Inc.,
which became the nation's fastest growing provider of NLP solutions in healthcare (showing up in
Deloitte's Technology
Fast 500 and
the Inc. 5000
listings); the company was acquired in April 2012
by 3M
Health Information Systems. I developed major pieces of the core
technology, helped build an excellent language technology team, and I
continue to advise on technology development and strategic
direction.
Somewhere along the way, much to my surprise, I was listed at #82 on the Future Health 100, a list of
"the most creative and influential innovators working in healthcare today"
at healthspottr.com. I don't do a great deal of academic research in this area
largely for reasons having to do with limited access to clinical data.
See my on-line list of publications for links
to papers on the above research topics and more.
Research Interests
Computational social science.
(Why? See my discussion of research and social impact.)
The key question I'm exploring: what can the signal available in language use tell us about underlying mental aspects of the speaker/author, such as their ideology, emotional state, or the presence of mental disorders? My work in this area has included topics such as sentiment analysis, persuasion, framing, and "spin", and I'm particularly interested in connections among lexical semantics, surface linguistic expression, and underlying internal state, as well as applications of unsupervised and semisupervised methods -- particularly topic modeling, because of those models' interpretability and their ability to incorporate pre-existing knowledge as informative priors.
Some additional areas of interest include:
Professional History
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Professional Activities
See also a more recently written followup: Why I Stopped Working on Machine Translation.
The Rest
Course Information
My regular teaching schedule generally includes a seminar in the fall,
and advanced computational linguistics in the spring.
Current semester
Previous courses
Computational Linguistics Colloquium Series
Other Links
Advice
Handy links
Click here if you want to arrange a meeting. Otherwise, in general, the best way to reach me is by e-mail to resnik [AT] umd _DOT_ edu.
Philip Resnik, Professor Department of Linguistics and Institute for Advanced Computer Studies 1401 Marie Mount Hall University of Maryland Department phone : (301) 405-7002 College Park, MD 20742 USA Department Fax : (301) 405-7104 http://umiacs.umd.edu/~resnik E-mail: : resnik [AT] umd _DOT_ edu UMIACS office: Iribe Center 4148
