# TEI2GO: A Multilingual Approach for Fast Temporal Expression Identification

arXiv: https://arxiv.org/abs/2403.16804 · HTML: https://mlsanity.com/paper/2403.16804/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019)
- [Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks](https://mlsanity.com/paper/1706.03993/) (2017)
- [Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text](https://mlsanity.com/paper/2005.09392/) (2020)
- [tieval: An Evaluation Framework for Temporal Information Extraction Systems](https://mlsanity.com/paper/2301.04643/) (2023)
- [XLTime: A Cross-Lingual Knowledge Transfer Framework for Temporal Expression Extraction](https://mlsanity.com/paper/2205.01757/) (2022)

## Built on by (most cited first, 1 of 1)

- [The Temporal Game: A New Perspective on Temporal Relation Extraction](https://mlsanity.com/paper/2509.00250/) (2025)

## Abstract (arXiv)

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.