  The understanding of time expressions includes two sub-tasks: recognition and
normalization. In recent years, significant progress has been made in the
recognition of time expressions while research on normalization has lagged
behind. Existing SOTA normalization methods highly rely on rules or grammars
designed by experts, which limits their performance on emerging corpora, such
as social media texts. In this paper, we model time expression normalization as
a sequence of operations to construct the normalized temporal value, and we
present a novel method called ARTime, which can automatically generate
normalization rules from training data without expert interventions.
Specifically, ARTime automatically captures possible operation sequences from
annotated data and generates normalization rules on time expressions with
common surface forms. The experimental results show that ARTime can
significantly surpass SOTA methods on the Tweets benchmark, and achieves
competitive results with existing expert-engineered rule methods on the
TempEval-3 benchmark.
