TY  - JOUR
T1  - One-Class Classification Model Based on Lexical Information and Syntactic Patterns
AU  - Lee, Hyeon-gu 
AU  - Choi, Maengsik 
AU  - Kim, Harksoo 
JO  - Journal of KIISE, JOK
PY  - 2015
DA  - 2015/1/14
DO  - 
KW  - Relation extraction
KW  - Distant supervision
KW  - One-class classification
KW  - Vector space
AB  - Relation extraction is an important information extraction technique that can be widely used in areas such as question-answering and knowledge population. Previous studies on relation extraction have been based on supervised machine learning models that need a large amount of training data manually annotated with relation categories. Recently, to reduce the manual annotation efforts for constructing training data, distant supervision methods have been proposed. However, these methods suffer from a drawback: it is difficult to use these methods for collecting negative training data that are necessary for resolving classification problems. To overcome this drawback, we propose a one-class classification model that can be trained without using negative data. The proposed model determines whether an input data item is included in an inner category by using a similarity measure based on lexical information and syntactic patterns in a vector space. In the experiments conducted in this study, the proposed model showed higher performance (an F1-score of 0.6509 and an accuracy of 0.6833) than a representative one-class classification model, one-class SVM(Support Vector Machine).