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    Machine Reading for Scientific Discovery

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    Author
    Hahn-Powell, Gus
    Issue Date
    2018
    Keywords
    assembly
    causal ordering
    hypothesis generation
    literature-based discovery
    machine reading
    Swanson linking
    Advisor
    Fong, Sandiway
    Surdeanu, Mihai
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Embargo
    Release after 01/05/2020
    Abstract
    The aim of this work is to accelerate scientific discovery by advancing machine reading approaches designed to extract claims and assertions made in the literature, assemble these statements into cohesive models, and generate novel hypotheses that synthesize findings from isolated research communities. Over 1 million new publications are added to the biomedical literature each year. This poses a serious challenge to researchers needing to understand the state of the field. It is effectively impossible for an individual to summarize the larger body of work or even remain abreast of research findings directly relevant to a subtopic. As the boundaries between disciplines continue to blur, the question of what to read grows more complicated. Researchers must inevitably turn to machine reading techniques to summarize findings, detect contradictions, and illuminate the inner workings of complex systems. Machine reading is a research program in artificial intelligence centered on teaching computers to read and comprehend natural language text. Through large-scale machine reading of the scientific literature, we can greatly advance our understanding of the natural world. Despite remarkable progress (Gunning et al., 2010; Berant et al., 2014; Cohen, 2015a), current machine reading systems face two major obstacles which impede wider adoption: <i>Assembly</i> The majority of machine reading systems extract disconnected findings from the literature (Berant et al., 2014). In areas of study such as biology, which involve large mechanistic systems with many interdependent components, it is essential that the insights scattered across the literature be contextualized and carefully integrated. The single greatest challenge facing machine reading is in learning to piece together this intricate puzzle to form coherent models and mitigate information overload. In this work, I will demonstrate how disparate biomolecular statements mined from text can be causally ordered into chains of reactions (Hahn-Powell et al., 2016b) that extend our understanding of mechanistic biology. Then, moving beyond a single domain, we will see how machine-read fragments (influence relations) drawn from a multitude of disciplines can be assembled into models of children’s heath. <i>Hypothesis generation and “undiscovered public knowledge”</i> (Swanson, 1986a) Without a notion of research communities and their interaction, machine reading systems struggle to identify knowledge gaps and key ideas capable of bridging disciplines and fostering the kind of collaboration that accelerates scientific progress. With this aim in mind, I introduce a procedure for detecting research communities using a large citation network and derive semantic representations that encode a measure of the flow of information between these groups. Finally, I leverage these representations to uncover influence relation pathways which connect otherwise isolated communities.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Graduate College
    Linguistics
    Degree Grantor
    University of Arizona
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    Dissertations

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