Title:
Interactive Visual Text Analytics
Interactive Visual Text Analytics
Author(s)
Kim, Hannah
Advisor(s)
Park, Haesun
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Abstract
Human-in-the-Loop machine learning leverages both human and machine intelligence to build a smarter model. Even with the advances in machine learning techniques, results generated by automated models can be of poor quality or do not always match users' judgment or context. To this end, keeping human in the loop via right interfaces to steer the underlying model can be highly beneficial. Prior research in machine learning and visual analytics has focused on either improving model performances or developing interactive interfaces without carefully considering the other side. In this dissertation, we design and develop interactive systems that tightly integrate algorithms, visualizations, and user interactions, focusing on improving interactivity, scalability, and interpretability of the underlying models. Specifically, we present three visual analytics systems to explore and interact with large-scale text data. First, we present interactive hierarchical topic modeling for multi-scale analysis of large-scale documents. Second, we introduce interactive search space reduction to discover relevant subset of documents with high recall for focused analyses. Lastly, we propose interactive exploration and debiasing of word embeddings.
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Date Issued
2020-12-07
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Resource Type
Text
Resource Subtype
Dissertation