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Title:Explanation mining
Author(s):Bhavya
Advisor(s):Zhai, ChengXiang
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):explanation mining
slide explainer
Abstract:In this thesis, we propose the idea of computational analysis of explanations. Explanations are used to provide an understanding of a concept, procedure or reasoning to others. Although explanations are present online ubiquitously within textbooks, videos, blogposts, discussion forums, and many more, there is no way to mine them automatically. As a result, users in need of an explanation have to rely on search engines and potentially read through multiple documents in an attempt to find a suitable explanation. This process can be highly tedious for them and may not even be successful in some cases. On the other hand, there are many users such as educators, authors, who write explanations and can benefit from assistants that help enhance the quality of their explanations. The goal of computational analysis of explanations is to assist both these kinds of users. In this work, our focus is on Explanation Mining to assist users seeking explanations. For understanding some of the linguistic features of explanations across multiple domains, we first apply standard Learning-to-rank models to rank explanations collected from the Explain Like I'm Five (ELI5) reddit forum. Based on cross-domain experiments, we find that a model trained on a sufficiently large dataset achieves decent performance across all domains which suggests that there are some common markers of explanations. Next, to apply this knowledge to the practical problem of mining explanations of educational concepts, we propose a baseline approach based on the popular Language Modeling approach of information retrieval. We show that incorporating knowledge from a model trained on the ELI5 dataset in the form of a document prior helps increase the performance of a standard retrieval model. This is encouraging because our method requires minimal in-domain supervision, as a result it can be deployed for multiple online courses. Finally, we show a demo system that acts as an assistant to online learners while viewing slides. The system enables users to select any piece of text on the slide and find an explanation for it. We conclude with some interesting directions for future work in this field.
Issue Date:2020-05-13
Type:Thesis
URI:http://hdl.handle.net/2142/108346
Rights Information:Copyright 2020 Bhavya
Date Available in IDEALS:2020-08-27
Date Deposited:2020-05


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