XTime: A general rule-based method for time expression recognition and normalization

Abstract

Time expression (a.k.a., timex) recognition and normalization (TERN) is a crucial task for downstream research. However, previous studies have overlooked the critical characteristics of timexes that significantly impact the task. To gain deeper insights, we conduct an analysis across four diverse English datasets to examine the key attributes of timex constituents. Our analysis reveals several noteworthy observations, such as: timexes tend to very short; the majority of timexes contain time tokens; there exist strong mapping relationships between time tokens and timex types; there exists a priority relationship among timex types; and timex values exhibit only some standard formats. Based on these insights, we propose a novel general rule-based method termed XTime11Source codes and datasets are available at https://github.com/xszhong/XTime. to recognize timexes from free text and normalize them into standard formats. Notably, XTime’s rules are designed in a general and heuristic manner, enabling its independence of diverse domains and text types. Experimental evaluations conducted on both in-domain and out-of-domain English datasets demonstrate that XTime consistently outperforms or performs comparably to representative state-of-the-art methods.