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        <title>Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture</title>
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    <abstract>In this paper, we propose to use a set of simple, uniform in architecture LSTM-based models to recover different kinds of temporal relations from text. Using the shortest dependency path between entities as input, the same architecture is used to extract intra-sentence, cross-sentence, and document creation time relations. A “double-checking” technique reverses entity pairs in classification, boosting the recall of positive cases and reducing misclassifications between opposite classes. An efficient pruning algorithm resolves conflicts globally. Evaluated on QA-TempEval (SemEval2015 Task 5), our proposed technique outperforms state-of-the-art methods by a large margin. We also conduct intrinsic evaluation and post state-of-the-art results on Timebank-Dense.</abstract>
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%0 Conference Proceedings
%T Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture
%A Meng, Yuanliang
%A Rumshisky, Anna
%A Romanov, Alexey
%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 meng-etal-2017-temporal
%X In this paper, we propose to use a set of simple, uniform in architecture LSTM-based models to recover different kinds of temporal relations from text. Using the shortest dependency path between entities as input, the same architecture is used to extract intra-sentence, cross-sentence, and document creation time relations. A “double-checking” technique reverses entity pairs in classification, boosting the recall of positive cases and reducing misclassifications between opposite classes. An efficient pruning algorithm resolves conflicts globally. Evaluated on QA-TempEval (SemEval2015 Task 5), our proposed technique outperforms state-of-the-art methods by a large margin. We also conduct intrinsic evaluation and post state-of-the-art results on Timebank-Dense.
%R 10.18653/v1/D17-1092
%U https://aclanthology.org/D17-1092
%U https://doi.org/10.18653/v1/D17-1092
%P 887-896

Markdown (Informal)

[Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture](https://aclanthology.org/D17-1092) (Meng et al., EMNLP 2017)

• Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture (Meng et al., EMNLP 2017)

ACL

• Yuanliang Meng, Anna Rumshisky, and Alexey Romanov. 2017. Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 887–896, Copenhagen, Denmark. Association for Computational Linguistics.