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A Framework for the Discovery and Tracking of Ideas in Longitudinal Text Corpora.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Framework for the Discovery and Tracking of Ideas in Longitudinal Text Corpora./
作者:	Mei, Mei.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	396 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-01, Section: B.
Contained By:	Dissertations Abstracts International84-01B.
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29282207
ISBN:	9798802751749

A Framework for the Discovery and Tracking of Ideas in Longitudinal Text Corpora.
Mei, Mei.

A Framework for the Discovery and Tracking of Ideas in Longitudinal Text Corpora. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 396 p.

Source: Dissertations Abstracts International, Volume: 84-01, Section: B.

Thesis (Ph.D.)--University of Cincinnati, 2022.

This item must not be sold to any third party vendors.

The emergence and evolution of ideas is one of the most important processes in human society, and has been a topic of great interest for philosophers and historians. Psychologists have also attempted to develop models of how new ideas arise from the recombination of existing ones, and have proposed to model this process as being similar to biological evolution. However, studying the evolution of ideas has been limited by the difficulty in obtaining systematic data. The recent exponential growth in electronic data promises a solution, but several impediments remain, including a systematic process for extracting ideas and methods for analyzing their dynamics over time.While the general problem of identifying ideas in texts is extremely complex, one possible approach is to look at how meaning is distributed in documents, and to study the evolution of this structure across documents and over time. The research in this dissertation develops a framework for doing this in large, longitudinal corpora of documents. This system, called the Framework for the Analysis of Semantic Structure Evolution in Text (FASSET), exploits the statistics of changing word usage within the corpus in combination with machine learning techniques, including topic analysis, semantic embedding, adaptive clustering, and dimensionality reduction for visualization. It represents an integrated model for extracting semantic structure within single documents, within text corpora with a single time-stamp, and across a longitudinally extensive corpus. It includes new methods for text segmentation, longitudinal topic identification, and longitudinal semantic clustering. The goal is to provide a system for exploring a simplified "systems biology" of ideas through which the evolution of ideas can be studied at various levels.The FASSET system is applied to two large longitudinal corpora: Speeches in the U.S. Congress over a period of 36 consecutive years, and papers presented at the International Joint Conference on Neural Networks (IJCNN) over 18 consecutive years. The results show what issues and ideas have animated the areas of American politics and neural networks over time, how various topics have evolved in that time, and how new topics have emerged from them.

ISBN: 9798802751749Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Longitudinal analysis

A Framework for the Discovery and Tracking of Ideas in Longitudinal Text Corpora.
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