Master of Science / Department of Computing and Information Sciences / William Hsu / This thesis examines automated genre classification in literature. The approach described uses text based comparison of book summaries to examine if word similarity is a feasible method for identifying genre types. Genres help users form impressions of what form a text will take. Knowing the genre of a literary work provides librarians, information scientists, and other users of a text collection with a summative guide to its form, its possible content, and what its members are about without having to peruse individual topic titles. This makes automatically generating genre labels a potentially useful tool in sorting unmarked text collections or searching the web.
This thesis provides a brief overview of the problems faced by researchers wishing to automate genre classification as well as the current work in the field. My own methodology will also be discussed. I implemented two basic methods for labeling genre. The results collected using them will be covered, as well as future work and improvements to the project that I wish to implement.
| Identifer | oai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/17578 |
| Date | January 1900 |
| Creators | Jordan, Emily |
| Publisher | Kansas State University |
| Source Sets | K-State Research Exchange |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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