A Cascaded Machine Learning Approach to Interpreting Temporal Expressions

  title={A Cascaded Machine Learning Approach to Interpreting Temporal Expressions},
  author={David Ahn and Joris van Rantwijk and Maarten de Rijke},
A new architecture for identifying and interpreting temporal expressions is introduced, in which the large set of complex hand-crafted rules standard in systems for this task is replaced by a series of machine learned classifiers and a much smaller set of context-independent semantic composition rules. Experiments with the TERN 2004 data set demonstrate that overall system performance is comparable to the state-of-the-art, and that normalization performance is particularly good. 
Highly Cited
This paper has 77 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

Explore Further: Topics Discussed in This Paper


Publications citing this paper.
Showing 1-10 of 44 extracted citations

Multilingual and cross-domain temporal tagging

Language Resources and Evaluation • 2013
View 3 Excerpts
Highly Influenced

Time expression normalization based on multi-scale classification and temporal focus model with hierarchical discourse transfer

2012 International Conference on Machine Learning and Cybernetics • 2012
View 3 Excerpts
Highly Influenced

Approaches to Temporal Expression Recognition in Hindi

ACM Trans. Asian & Low-Resource Lang. Inf. Process. • 2015

77 Citations

Citations per Year
Semantic Scholar estimates that this publication has 77 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.

Similar Papers

Loading similar papers…