Temporal information is important for many NLP tasks, and there has been extensive research on temporal tagging with a particular focus on English texts. Recently, other languages have also been addressed, e.g., HeidelTime was extended to process eight languages. Chinese temporal tagging has achieved less attention, and no Chinese temporal tagger is publicly available. In this paper, we address the full task of Chinese temporal tagging (extraction and normalization) by developing Chinese HeidelTime resources. Our evaluation on a publicly available corpus -which we also partially re-annotated due to its rather low quality -demonstrates the effectiveness of our approach, and we outperform a recent approach to normalize temporal expressions. The Chinese HeidelTime resource as well as the corrected corpus are made publicly available.
Li et al. REF proposed a Chinese temporal tagging (extraction and normalization) method by developing Chinese HeidelTime resources.
Chinese Temporal Tagging with HeidelTime
EACL
Computer Science