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Capturing Domain Semantics with Representation Learning: Applications to Health and Function

Newman-Griffis, Denis R
http://orcid.org/0000-0002-0473-4226
http://rave.ohiolink.edu/etdc/view?acc_num=osu1587658607378958

Abstract Details

2020, Doctor of Philosophy, Ohio State University, Computer Science and Engineering.
Natural language processing research is constantly expanding to new domains of text, new types of information, and new applications. A key factor for success in new settings is an ability to capture the characteristics of the language to be analyzed: i.e., the sublanguage of interest. One powerful tool for capturing information about language use is neural representation learning, a family of methods for mathematically representing words, phrases, and other units of language, based on usage patterns in large text corpora. Representation learning for language is predicated on the observation that lexical usage patterns convey important information about meaning, and models this information in terms of geometric relationships between lexical representations. Thus, learned representations provide a lens for analyzing and capturing patterns of language use within restricted domains, as well as for general applications. This thesis presents two main contributions to the literature. First, we present a method for moving beyond word-level information to learn representations of domain concepts from arbitrary text corpora. We demonstrate that these representations capture domain-relevant information about similarity and relatedness, for both biomedical and encyclopedic concepts, and show that they reveal clinically-significant differences in how medical concepts are discussed among different types of health documentation. We further show how concept-level representations learned using a variety of techniques can be effectively combined for semantic grounding of text. Second, we present the functional status domain as a new area for NLP analysis and application, with far-reaching impact in both healthcare delivery and social benefits administration. We define how functional status information is realized in practical language, and identify rehabilitation medicine documentation as a distinct sublanguage rich in functional status information. Finally, we show that a combination of neural representation learning from well-chosen data sources and modeling techniques informed by the characteristics of functional status information achieve high-quality extraction of mobility-related information from clinical data, helping to address issues of syntactic complexity and poor coverage in standardized vocabularies. We conclude by identifying future directions leading from our work, including broader application of representation-based analyses of differences in language use, combination of different representation strategies for NLP applications, and further analyses of the structure of functional status information to guide the development of new representation methods for this domain.
Eric Fosler-Lussier, PhD (Advisor)
Albert Lai, PhD (Committee Co-Chair)
Huan Sun, PhD (Committee Member)
Michael White, PhD (Committee Member)
356 p.
Artificial Intelligence; Computer Science; Health Care; Information Science; Linguistics; Rehabilitation
Natural language processing; artificial intelligence; computational semantics; disability and rehabilitation; medical informatics

Recommended Citations

Citations

  • Newman-Griffis, D. R. (2020). Capturing Domain Semantics with Representation Learning: Applications to Health and Function [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587658607378958

    APA Style (7th edition)

  • Newman-Griffis, Denis. Capturing Domain Semantics with Representation Learning: Applications to Health and Function. 2020. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1587658607378958.

    MLA Style (8th edition)

  • Newman-Griffis, Denis. "Capturing Domain Semantics with Representation Learning: Applications to Health and Function." Doctoral dissertation, Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587658607378958

    Chicago Manual of Style (17th edition)

osu1587658607378958
963
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