Jin-Woo Chung


2022

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Shedding New Light on the Language of the Dark Web
Youngjin Jin | Eugene Jang | Yongjae Lee | Seungwon Shin | Jin-Woo Chung
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

The hidden nature and the limited accessibility of the Dark Web, combined with the lack of public datasets in this domain, make it difficult to study its inherent characteristics such as linguistic properties. Previous works on text classification of Dark Web domain have suggested that the use of deep neural models may be ineffective, potentially due to the linguistic differences between the Dark and Surface Webs. However, not much work has been done to uncover the linguistic characteristics of the Dark Web. This paper introduces CoDA, a publicly available Dark Web dataset consisting of 10000 web documents tailored towards text-based Dark Web analysis. By leveraging CoDA, we conduct a thorough linguistic analysis of the Dark Web and examine the textual differences between the Dark Web and the Surface Web. We also assess the performance of various methods of Dark Web page classification. Finally, we compare CoDA with an existing public Dark Web dataset and evaluate their suitability for various use cases.

2018

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Feature Attention Network: Interpretable Depression Detection from Social Media
Hoyun Song | Jinseon You | Jin-Woo Chung | Jong C. Park
Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation

2017

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Extraction of Gene-Environment Interaction from the Biomedical Literature
Jinseon You | Jin-Woo Chung | Wonsuk Yang | Jong C. Park
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Genetic information in the literature has been extensively looked into for the purpose of discovering the etiology of a disease. As the gene-disease relation is sensitive to external factors, their identification is important to study a disease. Environmental influences, which are usually called Gene-Environment interaction (GxE), have been considered as important factors and have extensively been researched in biology. Nevertheless, there is still a lack of systems for automatic GxE extraction from the biomedical literature due to new challenges: (1) there are no preprocessing tools and corpora for GxE, (2) expressions of GxE are often quite implicit, and (3) document-level comprehension is usually required. We propose to overcome these challenges with neural network models and show that a modified sequence-to-sequence model with a static RNN decoder produces a good performance in GxE recognition.

2015

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Corpus annotation with a linguistic analysis of the associations between event mentions and spatial expressions
Jin-Woo Chung | Jinseon You | Jong C. Park
Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation

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CoMAGD: Annotation of Gene-Depression Relations
Rize Jin | Jinseon You | Jin-Woo Chung | Hee-Jin Lee | Maria Wolters | Jong Park
Proceedings of BioNLP 15