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Author ORCID Identifier


Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded


Month Degree Awarded


First Advisor

Brendan T. O'Connor

Subject Categories

Computational Engineering


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.


Creative Commons License

Creative Commons Attribution-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.