Off-campus UMass Amherst users: To download campus access dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.
Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan.
Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.
Author ORCID Identifier
Open Access Dissertation
Doctor of Philosophy (PhD)
Year Degree Awarded
Month Degree Awarded
Brendan T. O'Connor
People have been analyzing documents by reading keywords in context for centuries. Traditional approaches like paper concordances or digital keyword-in-context viewers display all occurrences of a single word from a corpus vocabulary amid immediately surrounding tokens or characters, to show readers how individual lexical items are used in bodies of text. We propose that these common tools are one particular application of a more general approach to analyzing documents, which we define as lexical corpus analysis. We then propose new natural language processing techniques for lexically-focused corpus investigation, and demonstrate how such methods can be used to create new user-facing tools for analyzing corpora.
Our contributions are divided into three parts. In Part 1, we consider how to represent a corpus lexicon to best reflect human mental and linguistic models of a domain, and propose a natural language processing (NLP) method for enriching a unigram corpus vocabulary with multiword phases. In Part 2, we consider how lexical systems might show query terms in context to best satisfy user search need, and offer several new techniques focused on summarizing mentions of a query term in context. Finally, in Part 3, we apply our proposed NLP methods towards new user-facing systems for lexical corpus analysis, and present user studies with journalists and historians which investigate how new lexical tools can help such users in their work.
Handler, Abram Kaufman, "Natural Language Processing for Lexical Corpus Analysis" (2021). Doctoral Dissertations. 2332.
Creative Commons License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.
Available for download on Tuesday, March 01, 2022