Changki Lee

Dependency-based semantic role labeling using sequence labeling with a structural SVM

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.

End-to-End Korean Part-of-Speech Tagging Using Copying Mechanism

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.

Balanced Korean Word Spacing with Structural SVM

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.