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.

本文介绍一种名为 ARTime 的新型时间表达式归一方法，它可以自动从训练数据中生成规则，避免了对领域专家的依赖，并在 Tweets 基准测试上显着超越了 SOTA 方法，并在 TempEval-3 基准测试上实现了与现有专家设计规则方法的竞争性结果。