TEI2GO: A Multilingual Approach for Fast Temporal Expression Identification
This addresses the bottleneck of limited runtime performance for large-scale applications in natural language processing, though it is incremental as it builds on existing methods like HeidelTime.
The paper tackles the problem of slow runtime in temporal expression identification systems, introducing TEI2GO models that match the effectiveness of HeidelTime while significantly improving runtime, supporting six languages and achieving state-of-the-art results in four of them.
Temporal expression identification is crucial for understanding texts written in natural language. Although highly effective systems such as HeidelTime exist, their limited runtime performance hampers adoption in large-scale applications and production environments. In this paper, we introduce the TEI2GO models, matching HeidelTime's effectiveness but with significantly improved runtime, supporting six languages, and achieving state-of-the-art results in four of them. To train the TEI2GO models, we used a combination of manually annotated reference corpus and developed ``Professor HeidelTime'', a comprehensive weakly labeled corpus of news texts annotated with HeidelTime. This corpus comprises a total of $138,069$ documents (over six languages) with $1,050,921$ temporal expressions, the largest open-source annotated dataset for temporal expression identification to date. By describing how the models were produced, we aim to encourage the research community to further explore, refine, and extend the set of models to additional languages and domains. Code, annotations, and models are openly available for community exploration and use. The models are conveniently on HuggingFace for seamless integration and application.