# Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture Source: [Temporal Information Extraction for Question Answering Using Syntactic Dependencies in an LSTM-based Architecture]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:48:18.501Z Authors: Yuanliang Meng, Anna Rumshisky, Alexey Romanov Published: 2017-03-17T00:02:42.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()