Changki Lee
Kangwon National University
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Publication
Featured researches published by Changki Lee.
Pattern Recognition Letters | 2013
Soojong Lim; Changki Lee; Dong-Yul Ra
Semantic Role Labeling (SRL) systems aim at determining the semantic role labels of the arguments of the predicates in natural language text. SRL systems can usually be built to work upon the result of constitient analysis (constituent-based), or dependency parsing (dependency-based). SRL systems can use either classification or sequence labeling as the main processing mechanism. In this paper, we show that a dependency-based SRL system using sequence labeling can achieve state-of-the-art performance when a new structural SVM adapted from the Pegasos algorithm is exploited for performing sequence labeling.
acm transactions on asian and low resource language information processing | 2018
Sangkeun Jung; Changki Lee; Hyunsun Hwang
In this article, we introduce a novel neural architecture for the end-to-end Korean Part-of-Speech (POS) tagging problem. To address the problem, we extend the present recurrent neural network-based sequence-to-sequence models to deal with the key challenges in this task: rare word generation and POS tagging. To overcome these issues, Input-Feeding and Copying mechanism are adopted. Although our approach does not require any manual features or preprocessed pattern matching dictionaries, our best single model achieves an F-score of 97.08. This is competitive with the current state-of-the-art model (F-score 98.03), which requires extensive manual feature processing.
empirical methods in natural language processing | 2014
Changki Lee; Edward Choi; Hyunki Kim
Most studies on statistical Korean word spacing do not utilize the information provided by the input sentence and assume that it was completely concatenated. This makes the word spacer ignore the correct spaced parts of the input sentence and erroneously alter them. To overcome such limit, this paper proposes a structural SVM-based Korean word spacing method that can utilize the space information of the input sentence. The experiment on sentences with 10% spacing errors showed that our method achieved 96.81% F-score, while the basic structural SVM method only achieved 92.53% F-score. The more the input sentence was correctly spaced, the more accurately our method performed.
Etri Journal | 2016
Cheoneum Park; Kyoung-Ho Choi; Changki Lee; Soojong Lim
IEICE Transactions on Information and Systems | 2017
Changki Lee
Journal of KIISE | 2015
Hyunsun Hwang; Changki Lee
IEICE Transactions on Information and Systems | 2013
Yoonjae Choi; Pum-Mo Ryu; Hyunki Kim; Changki Lee
Journal of KIISE | 2018
Hyunsun Hwang; Changki Lee; HyunKi Jang; Dongho Kang
Journal of KIISE | 2018
Hyunsun Hwang; Changki Lee
IEICE Transactions on Information and Systems | 2013
Changki Lee