Recognising and Interpreting Named Temporal Expressions

This paper introduces a new class of temporal expression – named temporal expressions – and methods for recognising and interpreting its members. The commonest temporal expressions typically contain date and time words, like April or hours. Research into recognising and interpreting these typical expressions is mature in many languages. However, there is a class of expressions that are less typical, very varied, and difficult to automatically interpret. These indicate dates and times, but are harder to detect because they often do not contain time words and are not used frequently enough to appear in conventional temporally-annotated corpora for example Michaelmas or Vasant Panchami.Using Wikipedia and linked data, we automatically construct a resource of English named temporal expressions, and use itto extract training examples from a large corpus. These examples are then used to train and evaluate a named temporal ex-pression recogniser. We also introduce and evaluate rules for automatically interpreting these expressions, and we observe thatuse of the rules improves temporal annotation performance over existing corpora This paper introduces a new class of temporal expression – named temporal expressions – and methods for recognising and interpreting its members. The commonest temporal expressions typically contain date and time words, like April or hours. Research into recognising and interpreting these typical expressions is mature in many languages. However, there is a class of expressions that are less typical, very varied, and difficult to automatically interpret. These indicate dates and times, but are harder to detect because they often do not contain time words and are not used frequently enough to appear in conventional temporally-annotated corpora – for example Michaelmas or Vasant Panchami. UsingWikipedia and linked data, we automatically construct a resource of English named temporal expressions, and use it to extract training examples from a largecorpus. These examples are then used to train and evaluate a named temporal expression recogniser. We also introduce and evaluate rules for automatically interpreting these expressions, and we observe that use of the rules improves temporal annotation performance over existing corpora.