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Interactive Visual Analytics System for Criminal Intelligence Analysts with Multiple Coordinated Views
Seokweon Jung, Donghwa Shin, Jinwook Bok, Seokhyeon Park, Hyeon Jeon, Jinwook Seo, Insoo Lee, Sooyoung Park
http://doi.org/10.5626/JOK.2023.50.1.47
Data that criminal intelligence analysts have to analyze have become much larger and more complex in recent decades. However, the environment and methods of investigation have not yet kept up with those changes. In this study, we examined current investigation practices in Korean Government Agency. We focused on the sensemaking process of investigation and tried to adopt visual analytics approaches for sensemaking into the investigation. We derived tasks and design requirements and designed a multi-view visual analytics system that could satisfy them. We validated our design with a high-fidelity prototype through a case study to show realistic use cases.
Method of Reflecting Various Personas in a Chatbot
Shinhyeok Oh, Seok-won Jung, Harksoo Kim
http://doi.org/10.5626/JOK.2021.48.2.160
A chatbot is a computer program that simulates human conversation. Research on generative chatbots that provide various responses based on personal characteristics has been increasing. Representatively, there are persona chatbots that reflect personal characteristics in chatbots. Persona chatbots refers to a chatbot that reflects persona, which means personal characteristics, and are gaining popularity due to the movement to reflect a brand personality in various services. In response to this trend, this paper proposes a chatbot model that can generate different responses for each persona by suggesting sentence persona encoder and table persona encoder that reflects personas based on dual WGAN generative chatbot. The performance of the proposed model is verified through comparative experiments and experimental examples for each module using quantitative and qualitative evaluation.
Document Summarization Using TextRank Based on Sentence Embedding
Seok-won Jeong, Jintae Kim, Harksoo Kim
http://doi.org/10.5626/JOK.2019.46.3.285
Document summarization is creating a short version document that maintains the main content of original document. An extractive summarization has been actively studied by the reason of it guarantees the basic level of grammar and high level of accuracy by copying a large amount of text from the original document. It is difficult to consider the meaning of sentences because the TextRank, which is a typical extractive summarization method, calculates an edge of graph through the frequency of words. In a bid to solve these drawbacks, we propose a new TextRank using sentence embedding. Through experiments, we confirmed that the proposed method can consider the meaning of the sentence better than the existing method.
A Semi-automatic Construction method of a Named Entity Dictionary Based on Wikipedia
Yeongkil Song, Seokwon Jeong, Harksoo Kim
A named entity(NE) dictionary is an important resource for the performance of NE recognition. However, it is not easy to construct a NE dictionary manually since human annotation is time consuming and labor-intensive. To save construction time and reduce human labor, we propose a semi-automatic system for the construction of a NE dictionary. The proposed system constructs a pseudo-document with Wiki-categories per NE class by using an active learning technique. Then, it calculates similarities between Wiki entries and pseudo-documents using the BM25 model, a well-known information retrieval model. Finally, it classifies each Wiki entry into NE classes based on similarities. In experiments with three different types of NE class sets, the proposed system showed high performance(macro-average F1-score of 0.9028 and micro-average F1-score 0.9554).
Construction of Korean Knowledge Base Based on Machine Learning from Wikipedia
Seok-won Jeong, Maengsik Choi, Harksoo Kim
The performance of many natural language processing applications depends on the knowledge base as a major resource. WordNet, YAGO, Cyc, and BabelNet have been extensively used as knowledge bases in English. In this paper, we propose a method to construct a YAGO-style knowledge base automatically for Korean (hereafter, K-YAGO) from Wikipedia and YAGO. The proposed system constructs an initial K-YAGO simply by matching YAGO to info-boxes in Wikipedia. Then, the initial K-YAGO is expanded through the use of a machine learning technique. Experiments with the initial K-YAGO shows that the proposed system has a precision of 0.9642. In the experiments with the expanded part of K-YAGO, an accuracy of 0.9468 was achieved with an average macro F1-measure of 0.7596.
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