TY  - JOUR
T1  - Korean Semantic Role Labeling Using Domain Adaptation Technique
AU  - Lim, Soojong 
AU  - Bae, Yongjin 
AU  - Kim, Hyunki 
AU  - Ra, Dongyul 
JO  - Journal of KIISE, JOK
PY  - 2015
DA  - 2015/1/14
DO  - 
KW  - domain adaptation
KW  - semantic role labeling
KW  - Prior model
KW  - simplified feature value
AB  - Developing a high-performance Semantic Role Labeling (SRL) system for a domain requires manually annotated training data of large size in the same domain. However, such SRL training data of sufficient size is available only for a few domains. Performances of Korean SRL are degraded by almost 15% or more, when it is directly applied to another domain with relatively small training data. This paper proposes two techniques to minimize performance degradation in the domain transfer. First, a domain adaptation algorithm for Korean SRL is proposed which is based on the prior model that is one of domain adaptation paradigms. Secondly, we proposed to use simplified features related to morphological and syntactic tags, when using small-sized target domain data to suppress the problem of data sparseness. Other domain adaptation techniques were experimentally compared to our techniques in this paper, where news and Wikipedia were used as the sources and target domains, respectively. It was observed that the highest performance is achieved when our two techniques were applied together. In our system"s performance, F1 score of 64.3% was considered to be 2.4~3.1% higher than the methods from other research.