Chinese Temporal Tagging with HeidelTime

Temporal information is important for many NLP tasks, and there has been ex-tensive research on temporal tagging with a particular focus on English texts. Re-cently, other languages have also been ad-dressed, e.g., HeidelTime was extended to process eight languages. Chinese temporal tagging has achieved less attention, and no Chinese temporal tagger is publicly avail-able. 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 effective-ness of our approach, and we outperform a recent approach to normalize temporal expressions. The Chinese HeidelTime re-source as well as the corrected corpus are made publicly available.