@InProceedings{zhong-sun-cambria:2017:Long,
  author    = {Zhong, Xiaoshi  and  Sun, Aixin  and  Cambria, Erik},
  title     = {Time Expression Analysis and Recognition Using Syntactic Token Types and General Heuristic Rules},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {420--429},
  abstract  = {Extracting time expressions from free text is a fundamental task for many
	applications. We analyze the time expressions from four datasets and find that
	only a small group of words are used to express time information, and the words
	in time expressions demonstrate similar syntactic behaviour. Based on the
	findings, we propose a type-based approach, named SynTime, to recognize time
	expressions. Specifically, we define three main syntactic token types, namely
	time token, modifier, and numeral, to group time-related regular expressions
	over tokens. On the types we design general heuristic rules to
	recognize time expressions. In recognition, SynTime first identifies the time
	tokens from raw text, then searches their surroundings for modifiers and
	numerals to form time segments, and finally merges the time segments to time
	expressions. As a light-weight rule-based tagger, SynTime runs in real time,
	and can be easily expanded by simply adding keywords for the text of different
	types and of different domains. Experiment on benchmark datasets and tweets
	data shows that SynTime outperforms state-of-the-art methods.},
  url       = {http://aclweb.org/anthology/P17-1039}
}