Jiseong Kim


2020

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Unsupervised Fact Checking by Counter-Weighted Positive and Negative Evidential Paths in A Knowledge Graph
Jiseong Kim | Key-sun Choi
Proceedings of the 28th International Conference on Computational Linguistics

Misinformation spreads across media, community, and knowledge graphs in the Web by not only human agents but also information extraction algorithms that extract factual statements from unstructured textual data to populate the existing knowledge graphs. Traditional fact checking by experts or crowds is increasingly difficult to keep pace with the volume of newly created misinformation in the Web. Therefore, it is important and necessary to enhance the computational ability to determine whether a given factual statement is truthful or not. We view this problem as a truth scoring task in a knowledge graph. We present a novel rule-based approach that finds positive and negative evidential paths in a knowledge graph for a given factual statement and calculates a truth score for the given statement by unsupervised ensemble of the found positive and negative evidential paths. For example, we can determine the factual statement “United States is the birth place of Barack Obama” as truthful if there is the positive evidential path (Barack Obama, birthPlace, Hawaii) ∧ (Hawaii, country, United States) in a knowledge graph. For another example, we can determine the factual statement “Canada is the nationality of Barack Obama” as untruthful if there is the negative evidential path (Barack Obama, nationality, United States) ∧ (United States, ≠, Canada) in a knowledge graph. For evaluating on a real-world situation, we constructed an evaluation dataset by labeling truth or untruth label on factual statements that were extracted from Wikipedia texts by using the state-of-the-art BERT-based information extraction system. Our evaluation results show that our approach outperforms the state-of-the-art unsupervised approaches significantly by up to 0.12 AUC-ROC and even outperforms the supervised approach by up to 0.05 AUC-ROC not only in our dataset but also in the two different standard datasets.

2018

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Semi-automatic Korean FrameNet Annotation over KAIST Treebank
Younggyun Hahm | Jiseong Kim | Sunggoo Kwon | Key-Sun Choi
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Unsupervised Korean Word Sense Disambiguation using CoreNet
Kijong Han | Sangha Nam | Jiseong Kim | Younggyun Hahm | Key-Sun Choi
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Automatic Wordnet Mapping: from CoreNet to Princeton WordNet
Jiseong Kim | Younggyun Hahm | Sunggoo Kwon | Key-Sun Choi
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

2016

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Dedicated Workflow Management for OKBQA Framework
Jiseong Kim | GyuHyeon Choi | Key-Sun Choi
Proceedings of the Open Knowledge Base and Question Answering Workshop (OKBQA 2016)

Nowadays, a question answering (QA) system is used in various areas such a quiz show, personal assistant, home device, and so on. The OKBQA framework supports developing a QA system in an intuitive and collaborative ways. To support collaborative development, the framework should be equipped with some functions, e.g., flexible system configuration, debugging supports, intuitive user interface, and so on while considering different developing groups of different domains. This paper presents OKBQA controller, a dedicated workflow manager for OKBQA framework, to boost collaborative development of a QA system.

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The Open Framework for Developing Knowledge Base And Question Answering System
Jiseong Kim | GyuHyeon Choi | Jung-Uk Kim | Eun-Kyung Kim | Key-Sun Choi
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations

Developing a question answering (QA) system is a task of implementing and integrating modules of different technologies and evaluating an integrated whole system, which inevitably goes with a collaboration among experts of different domains. For supporting a easy collaboration, this demonstration presents the open framework that aims to support developing a QA system in collaborative and intuitive ways. The demonstration also shows the QA system developed by our novel framework.