Temporal expressions

A temporal expression in a text is a sequence of tokens (words, numbers and characters) that denote time, that is express a point in time, a duration or a frequency. Examples:

A point in time:

He was born on <TIMEX>6 May, 1980</TIMEX>.

A duration:

The show lasted <TIMEX>7 minutes</TIMEX>.

A frequency:

The pump circulates the water <TIMEX>every 2 hours</TIMEX>.

Initially, temporal expressions were considered a type of named entities and their identification was part of the named entity recognition task. Since the Automatic Content Extraction program in 2004 there has been a separate task identified and called Temporal Expression Recognition and Normalisation (TERN). Timex evaluation is now evaluated in two major temporal annotation challenges: TempEval and i2b2, both of which prefer the TimeML-level TIMEX3 standard.<ref>See TIMEX3 at timeml.org</ref>

Approaches

Similarly to NER systems, temporal expression taggers have been created either using linguistic grammar-based techniques or statistical models. Hand-crafted grammar-based systems typically obtained better results, but at the cost of months of work by experienced linguists. There are many such systems available now,<ref>Strötgen, Jannik; Michael Gertz (2010). "HeidelTime: High quality rule-based extraction and normalization of temporal expressions". Proceedings of the 5th International Workshop on Semantic Evaluation. ACL.</ref><ref>Llorens, Hector; Leon Derczynski; Robert Gaizauskas; Estela Saquete (2012). "TIMEN: An Open Temporal Expression Normalisation Resource". LREC. ACL: 3044–3051.</ref><ref>Filannino, Michele; Gavin Brown; Goran Nenadic (2013). "ManTIME: Temporal identification and normalization in the TempEval-3 challenge". Proceedings of the 7th International Workshop on Semantic Evaluation. ACL.</ref> so creating a temporal expression recognizer from scratch is generally an undesirable duplication of effort. Instead, current approaches focus on novel subclasses of timex.<ref>Brucato, Matteo; Leon Derczynski; Hector Llorens; Kalina Bontcheva; Christian S. Jensen (2013). "Recognising and Interpreting Named Temporal Expressions" (PDF). Proceedings of the International Conference on Recent Advances in Natural Language Processing. ACL.</ref>

Statistical systems typically require a large amount of manually annotated training data and are usually applied to the recognition task only (although there is work done using machine learning algorithms to resolve certain ambiguities in the interpretation step).<ref>See, for example, Ahn, van Rantwijk & de Rijke 2007</ref><ref>Angeli, Gabor; Christoper Manning; Daniel Jurafsky (2012). "Parsing time: Learning to interpret time expressions" (PDF). Proceedings of NAACL. ACL: 446.</ref>

References

• Ahn, David; van Rantwijk, Joris; de Rijke, Maarten (2007), "A Cascaded Machine Learning Approach to Interpreting Temporal Expressions" (PDF), Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2007), ACL
• Filannino, Michele; Brown, Gavin; Nenadic, Goran (2013), "ManTIME: Temporal identification and normalization in the TempEval-3 challenge" (PDF), Proceedings of the 7th International Workshop on Semantic Evaluation, ACL