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 and for a good overview of my angle on this see my invited talk, Analyzing social media for suicide risk using natural language processing (~30min), at the AWS Machine Learning Summit; my recent article with suicide prevention experts also helps to situate what I do in its broader context. In addition to suicidology, a primary area of research in collaboration with computational and medical school colleagues involves computational methods for identifying signal related to depression and schizophrenia; you, dear reader, are invited to contribute data to this effort here.
In addition to research on computational methods, I've been trying hard to make progress on the ability of the wider computational community to work with sensitive mental health data, including creation of the The University of Maryland Reddit Suicidality Dataset, development of The UMD/NORC 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), and serving as co-founder and multiple-time organizer for the Workshops on Computational Linguistics and Clinical Psychology (CLPsych). Many of those pieces were recently brought together in the 2021 CLPsych Shared Task, where teams worked on prediction of suicide attempts using sensitive social media data within the UMD/NORC enclave. I've also recently joined the Scientific Advisory Committee for the Coleridge Initiative, a non-profit focused on data-driven policy decision-making, often involving sensitive data.
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. Most recently, I'm engaged in work with students Alexander Hoyle, Pranav Goel, and Ruipak Sarkar, and collaborator Kris Miler, on co-decisions, with the goal of using computational methods to better understand when and for what reasons individuals make the same versus different decisions.
With the same students I'm also engaged in an NSF RAPID project focused on improving topic modeling methods for analysis of open-ended survey responses, with the more general and ambitious goal of revolutionizing survey methodology by making open-ends a first-class citizen in survey research. This work is tightly connected to the COVID-19 pandemic: I've been collaborating on COVID-related survey research using computational techniques with folks at CDC National Center for National Center for Healthcare Statistics, the Pandemic Crisis Response Coalition, NYU School of Nursing, and others.
Computational psycholinguistics and neurolinguistics.
During the past several years, I have been re-engaging more fully with my
longstanding interests in computational approaches to cognitive questions, including psycholinguistics and more recently the computational neuroscience of language.
During my 2018-2019 sabbatical, I began getting up to speed on interests in computational cognitive neuroscience and in Fall 2019 I began 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. I've also begun collaborating with Christian Brodbeck (along with Ellen Lau and Jonathan Simon) on neural representations of continuous speech and linguistic context, using computational models as a way of expressing cognitive hypotheses.
I'm excited that these lines of work have begun to produce some interesting results, e.g. see here for recent work where we introduced a new predictive measure, topical surprisal, and used it in an fMRI to map the neural bases of broad and local
contextual prediction during natural language comprehension.
On the psycholinguistics side, I have long been interested in sentence processing and particularly the interaction of top-down prediction and bottom-up evidence; my paper on left-corner parsing and psychological plausibility is an early example. More recently, I've been looking at 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 initial 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 more recent collaboration with my colleague Colin Phillips and his student Hanna Muller.
(Also: folks who are interested in Bayesian models and psycholinguistics should also be talking with Naomi Feldman.)
Finally, I believe there are interesting ways to connect these cognitive modeling interests together with more application-oriented interests and particularly the computational social science interests discussed above. For a very big-picture look at how I'm thinking about this, take a look at my invited talk,
Beyond Facts: The Problem of Framing in Assessing What is True, at the 2020 Fact Extraction and Verification workshop (FEVER 3).
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
continued for a number of years after the acquisition 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 myself on medical records,
largely for reasons having to do with limited access to clinical data. The long-term crisis in data access for language-technology research on clinical data is the subject of what my wife calls my "Data Rant", which I have delivered for years in talks at venues including SXSW, the VA, NIH, and the National Academies. I've more or less given up on the idea of legal or policy changes that could help solve this problem, and instead I've been turning my attention to secure data enclaves as an alternative solution, the idea being to bring researchers to the data rather than disseminating the data out to researchers. This is the focus of the NORC/UMD Mental Health Data Enclave project. That said, I have recently begun collaborating with Katherine Goodman at the School of Medicine, who focuses on epidemiology and public health; we are working on a curated topic model for COVID-19 research literature and beginning to explore applying machine learning and other prediction model approaches in electronic health records.
See my on-line list of publications for links
to papers on the above research topics and more.
Currently, I'm serving as an advisor to FiscalNote, which provides predictive analytics related to legislation and regulation; SoloSegment, which provides behavior-based personalization technology in enterprise search; and
Converseon Inc., a leading provider for social listening and voice-of-customer analytics (for whom I spearheaded development of their original sentiment analysis platform).
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.
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
If you are an undergrad who would like to take one of my graduate level courses, here's what you need to do.
Previous courses
Computational Linguistics Colloquium Series
Other Links
Advice
Handy links
Miscellaneous other
Note that links here may be significantly out of date. This section may be most useful as a window into my odd ideas about what's worth putting up on my web page over the years.
Contact info
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
Oh, and by the way, my name is not spelled Philip Resnick,
Phillip Resnik, or Phillip Resnick, though this explicit disclaimer
may help people who don't know that find this page!
