Automatic Rule Generation for Time Expression Normalization
This addresses the limitation of expert-designed rules for time normalization, particularly for emerging corpora like social media texts, though it is incremental as it builds on existing normalization tasks.
The paper tackles the problem of time expression normalization, which lags behind recognition, by introducing ARTime, a method that automatically generates normalization rules from training data without expert intervention. ARTime significantly surpasses state-of-the-art methods on the Tweets benchmark and achieves competitive results on the TempEval-3 benchmark.
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.