UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

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Subject: search.filters.subject.Natural language processing

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    Developing and Measuring Latent Constructs in Text
    (2024) Hoyle, Alexander Miserlis; Resnik, Philip; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Constructs---like inflation, populism, or paranoia---are of fundamental concern to social science. Constructs are the vocabulary over which theory operates, and so a central activity is the development and measurement of latent constructs from observable data. Although the social sciences comprise fields with different epistemological norms, they share a concern for valid operationalizations that transparently map between data and measure. Economists at the US Bureau of Labor Statistics, for example, follow a hundred-page handbook to sample the egg prices that constitute the Consumer Price Index; Clinical psychologists rely on suites of psychometric tests to diagnose schizophrenia. In many fields, this observable data takes the form of language: as a social phenomenon, language data can encode many of the latent social constructs that people care about. Commensurate with both increasing sophistication in language technologies and amounts of available data, there has thus emerged a "text-as-data" paradigm aimed at "amplifying and augmenting" the analyses that compose research. At the same time, Natural Language Processing (NLP), the field from which analysis tools originate, has often remained separate from real-world problems and guiding theories---as least when it comes to social science. Instead, it focuses on atomized tasks under the assumption that progress on low-level language aspects will generalize to higher-level problems that involve overlapping elements. This dissertation focuses on NLP methods and evaluations that facilitate the development and measurement of latent constructs from natural language, while remaining sensitive to social sciences' need for interpretability and validity.
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    Towards Human-AI Cooperation on Sequential Decision Making Problems
    (2021) Feng, Shi; Boyd-Graber, Jordan; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The tools we use have a great impact on our productivity. It is imperative that tools are designed with the user’s objectives in mind. From self-driving cars to tackling misinformation, from machine translation to breast cancer diagnosis, we are relying more and more on tools with artificial intelligence (AI) powered by machine learning models. This thesis focuses on developing machine learning models that are maximally useful to humans. Our primary goal is to improve the productivity of human-AI cooperation on important decision making problems by understanding how human and AI interact. In the traditional approach to machine learning, humans are treated as either rivals or teachers. However, machine learning can make up for some of the shortcomings of humans. Treating humans as collaborators opens up several new directions of research. In the first part of the thesis, we use flashcard learning as a testbed and study how human productivity can benefit from passively consuming information generated by machine learning models. In the second part, we consider humans as active decision makers, and investigate how explanations of machine learning predictions can improve the performance of human-AI teams on sequential decision making problems. Finally, we study the limitations of natural language explanations for model predictions, as well as novel methods to improve them.
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    Relating lexical and syntactic processes in language: Bridging research in humans and machines
    (2018) Ettinger, Allyson; Phillips, Colin; Resnik, Philip; Linguistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Potential to bridge research on language in humans and machines is substantial - as linguists and cognitive scientists apply scientific theory and methods to understand how language is processed and represented by humans, computer scientists apply computational methods to determine how to process and represent language in machines. The present work integrates approaches from each of these domains in order to tackle an issue of relevance for both: the nature of the relationship between low-level lexical processes and syntactically-driven interpretation processes. In the first part of the dissertation, this distinction between lexical and syntactic processes focuses on understanding asyntactic lexical effects in online sentence comprehension in humans, and the relationship of those effects to syntactically-driven interpretation processes. I draw on computational methods for simulating these lexical effects and their relationship to interpretation processes. In the latter part of the dissertation, the lexical/syntactic distinction is focused on the application of semantic composition to complex lexical content, for derivation of sentence meaning. For this work I draw on methodology from cognitive neuroscience and linguistics to analyze the capacity of natural language processing systems to do vector-based sentence composition, in order to improve the capacities of models to compose and represent sentence meaning.