Jong Yoon Lee


2020

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Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains
Tarek Sakakini | Jong Yoon Lee | Aditya Duri | Renato F.L. Azevedo | Victor Sadauskas | Kuangxiao Gu | Suma Bhat | Dan Morrow | James Graumlich | Saqib Walayat | Mark Hasegawa-Johnson | Thomas Huang | Ann Willemsen-Dunlap | Donald Halpin
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

Healthcare systems have increased patients’ exposure to their own health materials to enhance patients’ health levels, but this has been impeded by patients’ lack of understanding of their health material. We address potential barriers to their comprehension by developing a context-aware text simplification system for health material. Given the scarcity of annotated parallel corpora in healthcare domains, we design our system to be independent of a parallel corpus, complementing the availability of data-driven neural methods when such corpora are available. Our system compensates for the lack of direct supervision using a biomedical lexical database: Unified Medical Language System (UMLS). Compared to a competitive prior approach that uses a tool for identifying biomedical concepts and a consumer-directed vocabulary list, we empirically show the enhanced accuracy of our system due to improved handling of ambiguous terms. We also show the enhanced accuracy of our system over directly-supervised neural methods in this low-resource setting. Finally, we show the direct impact of our system on laypeople’s comprehension of health material via a human subjects’ study (n=160).

2019

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Equipping Educational Applications with Domain Knowledge
Tarek Sakakini | Hongyu Gong | Jong Yoon Lee | Robert Schloss | JinJun Xiong | Suma Bhat
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

One of the challenges of building natural language processing (NLP) applications for education is finding a large domain-specific corpus for the subject of interest (e.g., history or science). To address this challenge, we propose a tool, Dexter, that extracts a subject-specific corpus from a heterogeneous corpus, such as Wikipedia, by relying on a small seed corpus and distributed document representations. We empirically show the impact of the generated corpus on language modeling, estimating word embeddings, and consequently, distractor generation, resulting in better performances than while using a general domain corpus, a heuristically constructed domain-specific corpus, and a corpus generated by a popular system: BootCaT.