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NSF Grants awarded to Adam Liter and Maria Polinsky
9/1/21

Support for research on constraints on movement, and on exceptive constructions.

Congratulations to Adam Liter and to Maria Polinsky, whose work has earned new support from the National Science Foundation. Adam has received a Doctoral Dissertation Research Improvement Grant for work with his supervisor, Jeff Lidz, on “Subjacency, the Empty Category Principle, and the nature of constraints on phrase movement.” Masha is the recipient of a Collaborative Research Award on “Variation in exceptive structures,” on how languages express thoughts like ‘everybody laughed except you,' a project on which Hisao Kurokami has already begun to work. See the abstracts below.

Adam Liter and Jeffrey Lidz, BCS #2116270, Subjacency, the Empty Category Principle, and the nature of constraints on phrase movement

In general, it is possible to form a question by 'moving' a wh-phrase like "who” or "which boy" out of a seemingly arbitrary number of clauses, as in "Who did Allie say that Amy saw?", "Who did Alicia hear that Allie said that Amy saw?", and so on. In these questions, "who" is the logical object of "saw" yet appears at the beginning of the sentence. However, there are certain syntactic environments, commonly called 'islands,' in which question formation is not possible. A question like "Who did the book by delight everyone?"--whose intended meaning is 'who is the person such that the book by that person delighted everyone'--sounds unnatural to speakers of English, suggesting that it is not a possible question despite having a reasonable meaning. Some linguists have claimed that these constraints disappear when the offending structure is elided, such as in a sentence like "Amy said that the book by someone delighted everyone, but I don't remember who". Such sentences sound a bit more natural to speakers of English, but their status isn't entirely clear. This dissertation project will advance linguistic theory by using recent experimental techniques to ascertain whether such sentences are grammatical. In advancing the field, this project will also support education and diversity by training an undergraduate research assistant in these experimental techniques, scientific thinking, and statistical analysis.

Using behavioral methods, this doctoral dissertation project probes the link between speakers' reported judgments and their sensitivity to structure in questions with and without ellipsis. The goal is to determine whether the same principles apply to dependencies involving ellipsis as those that do not, with the longer term goal of identifying the computational principles governing syntactic locality. More generally, the project addresses the consequences of mismatches between reported acceptability and subliminal sensitivity to structure in acceptability judgments.

Maria Polinsky, BCS #2116344, Variation in exceptive structures

All languages are able to express universal statements, even though we realize that they are seldom literally true. Consequently, languages also have means of expressing exceptions to such generalizations, via exceptive constructions. English examples include "Everybody but Sandy laughed" and "Everybody laughed except Sandy". Linguistic means of expressing exclusion have received modest attention from philosophers of language and semanticists, whose focus has been primarily on English. Beyond that small body of work, little is known about exceptive constructions across the world's languages: how they are built, what their distribution is within individual languages and across languages, and how they compare to other constructions expressing comparison or contrast. This research project fills this gap as the first cross-linguistic investigation of lexical, morphological, and syntactic properties of the construction. Understanding exceptive constructions allows linguists to create better theories of language structure and to predict the range of variation in natural languages; it helps computer scientists build better parsing models; it gives language educators new dimensions that should be emphasized in language teaching, and it provides cultural anthropologists with additional tools to study societal (dis)similarities in the concept of exclusion. 

This research project employs methodologies from linguistic typology, theoretical syntax, and formal semantics to carry out in-depth investigations of exceptive constructions in a wide range of the world's languages. The project aims for maximum linguistic coverage by using sampling techniques of modern linguistic typology. Theoretically, the project addresses a range of questions that arise from the empirical findings. In particular, it analyzes the contrast between free and connected exceptives, phrasal and clausal exceptives, and coordinated and subordinated exceptives. The project develops diagnostics that reliably identify the different types of exceptives and identifies independent linguistic properties that correlate with these different types of exceptives in a language. Therefore, it allows researchers to predict the type of exceptive constructions in an individual language. Beyond developing a picture of exceptive structure cross-linguistically, the project has notable implications for current theories of ellipsis. The project provides data on low-resource and endangered languages and highlights the importance of linguistic diversity for a complete understanding of the human language system.

 

How Tech Can Fill Gaps in Mental Health Care
8/18/21

By Chris Carroll

As the clouds of mental illness gather, it can be difficult for patients to recognize their own symptoms and find necessary help to navigate storms like episodes of depression or schizophrenia.

With $1.2 million in new funding from the National Science Foundation, University of Maryland researchers are creating a computerized framework that could one day lead to a system capable of a mental weather forecast of sorts. It would meld language and speech analysis with machine learning and clinical expertise to help patients and mental health clinicians connect and head off crises while dealing with a sparsely resourced U.S. mental health care system.

“We’re addressing what has been called the ‘clinical white space’ in mental health care, when people are between appointments and their doctors have little ability to help monitor what’s happening with them,” said Philip Resnik, a professor of linguistics with a joint appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS) who is helping to lead the research.

The project was born with the help of a seed grant through the AI + Medicine for High Impact (AIM-HI) Challenge Awards, which bring together scholars at the University of Maryland, College Park (UMCP) with medical researchers at the University of Maryland, Baltimore (UMB) on major research initiatives that link artificial intelligence and medicine. Deanna Kelly, a professor of psychiatry at the University of Maryland School of Medicine, is another of the project’s leaders, as are electrical and computer engineering Professor Carol-Espy Wilson and computer science Assistant Professor John Dickerson, both at UMCP.

The new funding will help the research team pour their diverse expertise into a single framework, which would then be developed into a deployable system for testing in a clinical setting.

How would such a system work? Users might answer a series of questions about physical and emotional well-being, with the system employing artificial intelligence to analyze word choice and language use—Resnik’s area of focus in the project. It could also monitor the patient’s speech patterns, analyzing changes in the timing and degree of movement made by the lips and different parts of the tongue, and comparing it to a baseline sample taken from healthy control subjects or earlier when the participant was in remission, said Espy-Wilson, who has an appointment in the Institute for Systems Research.

People generally overlap neighboring sounds when speaking, beginning the next sound before finishing the previous one, a process called co-production. But someone suffering from depression, for instance, has simpler coordination, and their sounds don’t overlap to the same extent.

“You can't think as fast, you can't talk as fast when you’re depressed,” said Espy-Wilson. “And when you talk, you have more and longer pauses … You have to think more about what you want to say. The more depressed you are, the more of the psychomotor slowing you're going to have.”

While the final form of the system has yet to take shape, it could potentially live in an app on patients’ phones, and with their permission, automatically monitor their mental state and determine their level of need for clinical intervention, as well as what resources are available to help.

If the system simply directed streams of patients at already overloaded doctors or facilities with no open beds, it could potentially make things worse for everyone, said Dickerson, who has a joint appointment in UMIACS.

He’s adding his expertise to work that Resnik and Espy-Wilson have been pursuing for years, and taking on the central challenge—using an approach known in the machine learning field as the “multi-armed bandit” problem—of creating a system that can deploy limited clinical resources while simultaneously determining how to best meet a range of evolving patient needs. During development and testing, the AI system’s determinations will always be monitored by a human overseer, said Dickerson.

The World Health Organization estimated a decade ago that the cost of treating mental health issues between 2011 and 2030 would top $16 trillion worldwide, exceeding cardiovascular diseases. The stresses of the COVID-19 pandemic have exacerbated an already high level of need, and in some cases resulted in breakdown conditions for the system, said Kelly, director of the Maryland Psychiatric Research Center’s Treatment Research Program.

As the project develops, the technology could not only connect patients with a higher level of care to prevent worsening problems (avoiding costlier care), but also might help clinicians understand which patients don’t need hospitalization. Living in the community with necessary supports is often healthier than staying in a psychiatric facility—plus it’s cheaper and frees up a hospital bed for someone who needs it, she said.

“Serious mental illness makes up a large portion of health care costs here in the U.S. and around the world,” Kelly said. “Finding a way to assist clinicians in preventing relapses and keeping people well could dramatically improve people’s lives, as well as save money.”

Aadit Tambe M.Jour. ’22 contributed to this article.

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