TEI2GO: A Multilingual Approach for Fast Temporal Expression
  Identification
Hugo Sousa Ricardo Campos Alípio Jorge
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.