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Natural Language Generation : = Improving the Accessibility of Causal Modeling Through Applied Deep Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Natural Language Generation :/
Reminder of title:	Improving the Accessibility of Causal Modeling Through Applied Deep Learning.
Author:	Gandee, Tyler John.
Description:	1 online resource (103 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-11.
Contained By:	Masters Abstracts International85-11.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798382324081

Natural Language Generation : = Improving the Accessibility of Causal Modeling Through Applied Deep Learning.
Gandee, Tyler John.

Natural Language Generation :Improving the Accessibility of Causal Modeling Through Applied Deep Learning. - 1 online resource (103 pages)

Source: Masters Abstracts International, Volume: 85-11.

Thesis (M.S.)--Miami University, 2024.

Includes bibliographical references

Causal maps are graphical models that are well-understood in small scales. When created through a participatory modeling process, they become a strong asset in decision making. Furthermore, those who participate in the modeling process may seek to understand the problem from various perspectives. However, as causal maps increase in size, the information they contain becomes clouded, which results in the map being unusable. In this thesis, we transform causal maps into various mediums to improve the usability and accessibility of large causal models; our proposed algorithms can also be applied to small-scale causal maps. In particular, we transform causal maps into meaningful paragraphs using GPT and network traversal algorithms to attain full-coverage of the map. Then, we compare automatic text summarization models with graph reduction algorithms to reduce the amount of text to a more approachable size. Finally, we combine our algorithms into a visual analytics environment to provide details-on-demand for the user by displaying the summarized text, and interacting with summaries to display the detailed text, causal map, and even generate images in an appropriate manner. We hope this research provides more tools for decisionmakers and allows modelers to give back to participants the final result of their work.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382324081Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Decision makingIndex Terms--Genre/Form:

554714
Electronic books.

Natural Language Generation : = Improving the Accessibility of Causal Modeling Through Applied Deep Learning.
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