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        <title>A Structured Learning Approach to Temporal Relation Extraction</title>
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    <abstract>Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challenging problem even for human annotators. This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. As a byproduct, this provides a new perspective on handling missing relations, a known issue that hurts existing methods. As we show, the proposed approach results in significant improvements on the two commonly used data sets for this problem.</abstract>
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%0 Conference Proceedings
%T A Structured Learning Approach to Temporal Relation Extraction
%A Ning, Qiang
%A Feng, Zhili
%A Roth, Dan
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F ning-etal-2017-structured
%X Identifying temporal relations between events is an essential step towards natural language understanding. However, the temporal relation between two events in a story depends on, and is often dictated by, relations among other events. Consequently, effectively identifying temporal relations between events is a challenging problem even for human annotators. This paper suggests that it is important to take these dependencies into account while learning to identify these relations and proposes a structured learning approach to address this challenge. As a byproduct, this provides a new perspective on handling missing relations, a known issue that hurts existing methods. As we show, the proposed approach results in significant improvements on the two commonly used data sets for this problem.
%R 10.18653/v1/D17-1108
%U https://aclanthology.org/D17-1108
%U https://doi.org/10.18653/v1/D17-1108
%P 1027-1037

Markdown (Informal)

[A Structured Learning Approach to Temporal Relation Extraction](https://aclanthology.org/D17-1108) (Ning et al., EMNLP 2017)

• A Structured Learning Approach to Temporal Relation Extraction (Ning et al., EMNLP 2017)

ACL

• Qiang Ning, Zhili Feng, and Dan Roth. 2017. A Structured Learning Approach to Temporal Relation Extraction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1027–1037, Copenhagen, Denmark. Association for Computational Linguistics.