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

In this paper, we propose to use a set of simple, uniform in architecture LSTMbased models to recover different kinds of temporal relations from text. Using the shortest dependency path between entities as input, the same architecture is implemented to extract intra-sentence, crosssentence, 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-ofthe-art methods by a large margin. We also conduct intrinsic evaluation and post stateof-the-art results on Timebank-Dense.