Yu Seop Kim

Also published as: Yu-Seop Kim


2020

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Various Approaches for Predicting Stroke Prognosis using Magnetic Resonance Imaging Text Records
Tak-Sung Heo | Chulho Kim | Jeong-Myeong Choi | Yeong-Seok Jeong | Yu-Seop Kim
Proceedings of the 3rd Clinical Natural Language Processing Workshop

Stroke is one of the leading causes of death and disability worldwide. Stroke is treatable, but it is prone to disability after treatment and must be prevented. To grasp the degree of disability caused by stroke, we use magnetic resonance imaging text records to predict stroke and measure the performance according to the document-level and sentence-level representation. As a result of the experiment, the document-level representation shows better performance.

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Lightweight Text Classifier using Sinusoidal Positional Encoding
Byoung-Doo Oh | Yu-Seop Kim
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing

Large and complex models have recently been developed that require many parameters and much time to solve various problems in natural language processing. This paper explores an efficient way to avoid models being too complicated and ensure nearly equal performance to models showing the state-of-the-art. We propose a single convolutional neural network (CNN) using the sinusoidal positional encoding (SPE) in text classification. The SPE provides useful position information of a word and can construct a more efficient model architecture than before in a CNN-based approach. Our model can significantly reduce the parameter size (at least 67%) and training time (up to 85%) while maintaining similar performance to the CNN-based approach on multiple benchmark datasets.

2017

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Correlation Analysis of Chronic Obstructive Pulmonary Disease (COPD) and its Biomarkers Using the Word Embeddings
Byeong-Hun Yoon | Yu-Seop Kim
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

It is very costly and time consuming to find new biomarkers for specific diseases in clinical laboratories. In this study, to find new biomarkers most closely related to Chronic Obstructive Pulmonary Disease (COPD), which is widely known as respiratory disease, biomarkers known to be associated with respiratory diseases and COPD itself were converted into word embedding. And their similarities were measured. We used Word2Vec, Canonical Correlation Analysis (CCA), and Global Vector (GloVe) for word embedding. In order to replace the clinical evaluation, the titles and abstracts of papers retrieved from Google Scholars were analyzed and quantified to estimate the performance of the word em-bedding models.

2016

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Drop-out Conditional Random Fields for Twitter with Huge Mined Gazetteer
Eunsuk Yang | Young-Bum Kim | Ruhi Sarikaya | Yu-Seop Kim
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Hallym: Named Entity Recognition on Twitter with Word Representation
Eun-Suk Yang | Yu-Seop Kim
Proceedings of the Workshop on Noisy User-generated Text

2014

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Training a Korean SRL System with Rich Morphological Features
Young-Bum Kim | Heemoon Chae | Benjamin Snyder | Yu-Seop Kim
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2002

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A Comparative Evaluation of Data-driven Models in Translation Selection of Machine Translation
Yu-Seop Kim | Jeong-Ho Chang | Byoung-Tak Zhang
COLING 2002: The 19th International Conference on Computational Linguistics

2000

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Machine translation systems: E-K, K-E, J-K, K-J
Yu Seop Kim | Sung Dong Kim | Seong Bae Park | Jong Woo Lee | Jeong Ho Chang | Kyu Baek Hwang | Min O Jang | Yung Taek Kim
Proceedings of the Fourth Conference of the Association for Machine Translation in the Americas: User Studies

We present four kinds of machine translation system in this description: E-K (English to Korean), K-E (Korean to English), J-K (Japanese to Korean), K-J (Korean to Japanese). Among these, E-K and K-J translation systems are published commercially, and the other systems have finished their development. This paper describes the structure and function of each system with figures and translation results.