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Buckets full of words: Applying auto...

Kersey, Lauren Jenna Elise.

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Buckets full of words: Applying automatic attribution analysis towards the task of time periodization.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Buckets full of words: Applying automatic attribution analysis towards the task of time periodization./
作者:	Kersey, Lauren Jenna Elise.
面頁冊數:	67 p.
附註:	Source: Masters Abstracts International, Volume: 55-04.
Contained By:	Masters Abstracts International55-04(E).
標題:	Modern language. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10096540
ISBN:	9781339606484

Buckets full of words: Applying automatic attribution analysis towards the task of time periodization.
Kersey, Lauren Jenna Elise.

Buckets full of words: Applying automatic attribution analysis towards the task of time periodization. - 67 p.

Source: Masters Abstracts International, Volume: 55-04.

Thesis (M.A.)--Saint Louis University, 2015.

The following thesis is a transcript of a three podcast episodes. These podcast episodes summarize the purpose, the plan, and the initial results for an interdisciplinary data science project that I started at Saint Louis University in the fall of 2014. This interdisciplinary project, called the Text Mining Initiative (TMI), includes graduate and undergraduate students of English, Math, and Computer Science. TMI uses data mining, machine learning, natural language processing, and rhetorical theory to explore new approaches to the task of time periodization. Scholars who study historical documents often break up large continuums of time into smaller chunks (time periods). The assumption is often that the properties of these documents (the word forms, for example) change significantly over time yet remain consistent or predictable when scholars observe these smaller chunks. For any large span of time where n equals the number of years, the number of possible time period combinations (meaning the possible combinations of sequential, non-overlapping years) increases exponentially. This project asks whether new technologies can help us select useful categories from these many possibilities. In sum, this project applies the general approach of automatic text classification to experiment with organizing texts into different buckets based on the changes they exhibit over time.

ISBN: 9781339606484Subjects--Topical Terms:

3174390
Modern language.

Buckets full of words: Applying automatic attribution analysis towards the task of time periodization.
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520 $a The following thesis is a transcript of a three podcast episodes. These podcast episodes summarize the purpose, the plan, and the initial results for an interdisciplinary data science project that I started at Saint Louis University in the fall of 2014. This interdisciplinary project, called the Text Mining Initiative (TMI), includes graduate and undergraduate students of English, Math, and Computer Science. TMI uses data mining, machine learning, natural language processing, and rhetorical theory to explore new approaches to the task of time periodization. Scholars who study historical documents often break up large continuums of time into smaller chunks (time periods). The assumption is often that the properties of these documents (the word forms, for example) change significantly over time yet remain consistent or predictable when scholars observe these smaller chunks. For any large span of time where n equals the number of years, the number of possible time period combinations (meaning the possible combinations of sequential, non-overlapping years) increases exponentially. This project asks whether new technologies can help us select useful categories from these many possibilities. In sum, this project applies the general approach of automatic text classification to experiment with organizing texts into different buckets based on the changes they exhibit over time.
520 $a This podcast is an additional experiment using audio as a medium for explaining text mining technologies. The first episode presents the research question, outlines the research process, and provides an introduction to the fields of data mining and machine learning. Episode II is a closer look at k-means clustering as one possible approach to periodization, and it introduces the audience to the project's suite of tools. The final episode attempts to interpret these results and concludes by making plans for future steps. Episode I follows my process of learning from the computer scientists on the team; Episode II follows my attempt to share this new knowledge with other English graduate students; and Episode III illustrates how the complexity of these new tools and methods can produce results with their own unique brand of confusion and ambiguity.
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