Temporal Information Extraction from Korean Texts
Young-Seob Jeong, Zae Myung Kim, Hyun-Woo Do, Chae-Gyun Lim, Ho‐Jin Choi · 2015
As documents tend to contain temporal information, extracting such information is attracting much research interests recently.In this paper, we propose a hybrid method that combines machine-learning models and hand-crafted rules for the task of extracting temporal information from unstructured Korean texts.We address Korean-specific research issues and propose a new probabilistic model to generate complementary features.The performance of our approach is demonstrated by experiments on the TempEval-2 dataset, and the Korean TimeBank dataset which we built for this study.