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arXiv · 1703.05851

Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture

Yuanliang Meng·Anna Rumshisky·Alexey Romanov
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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.

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Yuanliang Meng, Anna Rumshisky, Alexey Romanov. 2017-10-05. Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture. https://arxiv.org/abs/1703.05851

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