Kijong Han


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Effective Crowdsourcing of Multiple Tasks for Comprehensive Knowledge Extraction
Sangha Nam | Minho Lee | Donghwan Kim | Kijong Han | Kuntae Kim | Sooji Yoon | Eun-kyung Kim | Key-Sun Choi
Proceedings of the 12th Language Resources and Evaluation Conference

Information extraction from unstructured texts plays a vital role in the field of natural language processing. Although there has been extensive research into each information extraction task (i.e., entity linking, coreference resolution, and relation extraction), data are not available for a continuous and coherent evaluation of all information extraction tasks in a comprehensive framework. Given that each task is performed and evaluated with a different dataset, analyzing the effect of the previous task on the next task with a single dataset throughout the information extraction process is impossible. This paper aims to propose a Korean information extraction initiative point and promote research in this field by presenting crowdsourcing data collected for four information extraction tasks from the same corpus and the training and evaluation results for each task of a state-of-the-art model. These machine learning data for Korean information extraction are the first of their kind, and there are plans to continuously increase the data volume. The test results will serve as an initiative result for each Korean information extraction task and are expected to serve as a comparison target for various studies on Korean information extraction using the data collected in this study.


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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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A Korean Knowledge Extraction System for Enriching a KBox
Sangha Nam | Eun-kyung Kim | Jiho Kim | Yoosung Jung | Kijong Han | Key-Sun Choi
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

The increased demand for structured knowledge has created considerable interest in knowledge extraction from natural language sentences. This study presents a new Korean knowledge extraction system and web interface for enriching a KBox knowledge base that expands based on the Korean DBpedia. The aim is to create an endpoint where knowledge can be extracted and added to KBox anytime and anywhere.

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Utilizing Graph Measure to Deduce Omitted Entities in Paragraphs
Eun-kyung Kim | Kijong Han | Jiho Kim | Key-Sun Choi
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations

This demo deals with the problem of capturing omitted arguments in relation extraction given a proper knowledge base for entities of interest. This paper introduces the concept of a salient entity and use this information to deduce omitted entities in the paragraph which allows improving the relation extraction quality. The main idea to compute salient entities is to construct a graph on the given information (by identifying the entities but without parsing it), rank it with standard graph measures and embed it in the context of the sentences.