Recognising and Interpreting Named Temporal Expressions

TY - GEN

T1 - Recognising and Interpreting Named Temporal Expressions

AU - Brucato, Matteo

AU - Derczynski, Leon

AU - Llorens, Hectjor

AU - Bontcheva, Kallina

AU - Jensen, Christian S.

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

M3 - Article in proceedings

SP - 113

EP - 122

BT - Proceedings of Recent Advances in Natural Language Processing

A2 - Angelova, Galia

A2 - Bontcheva, Kalina

A2 - Mitkov, Ruslan

PB - INCOMA Ltd

T2 - Recent Advances in Natural Language Processing

Y2 - 7 September 2013 through 13 September 2013

ER -