TIMEX3 and event extraction using recurrent neural networks

Abstract

This paper investigates the performance of Elman-type and Jordan-type recurrent neural networks (RNN) in extracting temporal information from textual data. The RNN architectures are applied to two tasks of TempEval-2 challenge: (1) extracting the extent of TIMEX3 tags and its TYPE, and (2) extracting the extent of EVENT tags and its CLASS attribute. For the first task, the performances of the RNN models are highly comparable to that of the wining entry for the challenge. For the second task, both models outperform the winning entry, attaining nearly full scores.