# Professor HeidelTime

[![Paper](https://img.shields.io/badge/Paper-557C55)](https://dl.acm.org/doi/10.1145/3583780.3615130)
[![GitHub](https://img.shields.io/badge/GitHub-A6CF98)](https://github.com/hmosousa/professor_heideltime)

Professor HeidelTime is a project to create a multilingual corpus weakly labeled with [HeidelTime](https://github.com/HeidelTime/heideltime), a temporal tagger.

## Corpus Details

The weak labeling was performed in six languages. Here are the specifics of the corpus for each language:

| Dataset                 | Language | Documents | From       | To         | Tokens     | Timexs    |
| ----------------------- | -------- | --------- | ---------- | ---------- | ---------- | --------  |
| All the News 2.0        | EN       | 24,642    | 2016-01-01 | 2020-04-02 | 18,755,616 | 254,803   |
| Italian Crime News      | IT       | 9,619     | 2011-01-01 | 2021-12-31 | 3,296,898  | 58,823    |
| German News Dataset     | DE       | 33,266    | 2003-01-01 | 2022-12-31 | 21,617,888 | 348,011   |
| ElMundo News            | ES       | 19,095    | 2005-12-02 | 2021-10-18 | 12,515,410 | 194,043   |
| French Financial News   | FR       | 24,293    | 2017-10-19 | 2021-03-19 | 1,673,053  | 83,431    |
| Público News            | PT       | 27,154    | 2000-11-14 | 2002-03-20 | 5,929,377  | 111,810   |

## Contact

For more information, reach out to [Hugo Sousa](https://hugosousa.net) at <hugo.o.sousa@inesctec.pt>.

This framework is a part of the [Text2Story](https://text2story.inesctec.pt) project. This project is financed by the ERDF – European Regional Development Fund through the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project PTDC/CCI-COM/31857/2017 (NORTE-01-0145-FEDER-03185).

## Cite

If you use this work, please cite the following [paper](https://dl.acm.org/doi/10.1145/3583780.3615130):

```bibtex
@inproceedings{10.1145/3583780.3615130,
    author = {Sousa, Hugo and Campos, Ricardo and Jorge, Al\'{\i}pio},
    title = {TEI2GO: A Multilingual Approach for Fast Temporal Expression Identification},
    year = {2023},
    isbn = {9798400701245},
    publisher = {Association for Computing Machinery},
    url = {https://doi.org/10.1145/3583780.3615130},
    doi = {10.1145/3583780.3615130},
    booktitle = {Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
    pages = {5401–5406},
    numpages = {6},
    keywords = {temporal expression identification, multilingual corpus, weak label},
    location = {Birmingham, United Kingdom},
    series = {CIKM '23}
}
```

task_categories:token-classification task_ids:parsing task_ids:part-of-speech task_ids:named-entity-recognition annotations_creators:machine-generated language_creators:found multilinguality:multilingual size_categories:100K<n<1M source_datasets:original language:en language:fr language:pt language:de language:it language:es license:mit Timex Timexs Temporal Expression Temporal Expressions Temporal Information Timex Identification Timex Classification Timex Extraction doi:10.57967/hf/1346 region:us
{"annotations_creators": ["machine-generated"], "language_creators": ["found"], "language": ["en", "fr", "pt", "de", "fr", "it", "es"], "license": ["mit"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["token-classification"], "task_ids": ["parsing", "part-of-speech", "named-entity-recognition"], "pretty_name": "Professor HeidelTime", "tags": ["Timex", "Timexs", "Temporal Expression", "Temporal Expressions", "Temporal Information", "Timex Identification", "Timex Classification", "Timex Extraction"], "configs": [{"config_name": "portuguese", "data_files": "portuguese.json"}, {"config_name": "english", "data_files": "english.json"}, {"config_name": "french", "data_files": "french.json"}, {"config_name": "italian", "data_files": "italian.json"}, {"config_name": "spanish", "data_files": "spanish.json"}, {"config_name": "german", "data_files": "german.json"}]}
TAGS
#task_categories-token-classification #task_ids-parsing #task_ids-part-of-speech #task_ids-named-entity-recognition #annotations_creators-machine-generated #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-English #language-French #language-Portuguese #language-German #language-Italian #language-Spanish #license-mit #Timex #Timexs #Temporal Expression #Temporal Expressions #Temporal Information #Timex Identification #Timex Classification #Timex Extraction #doi-10.57967/hf/1346 #region-us 

Professor HeidelTime
====================


![Paper](URL
![GitHub](URL


Professor HeidelTime is a project to create a multilingual corpus weakly labeled with HeidelTime, a temporal tagger.


Corpus Details
--------------


The weak labeling was performed in six languages. Here are the specifics of the corpus for each language:



Contact
-------


For more information, reach out to Hugo Sousa at [hugo.o.sousa@URL](mailto:hugo.o.sousa@URL).


This framework is a part of the Text2Story project. This project is financed by the ERDF – European Regional Development Fund through the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project PTDC/CCI-COM/31857/2017 (NORTE-01-0145-FEDER-03185).


Cite
----


If you use this work, please cite the following paper:



TAGS
#task_categories-token-classification #task_ids-parsing #task_ids-part-of-speech #task_ids-named-entity-recognition #annotations_creators-machine-generated #language_creators-found #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-English #language-French #language-Portuguese #language-German #language-Italian #language-Spanish #license-mit #Timex #Timexs #Temporal Expression #Temporal Expressions #Temporal Information #Timex Identification #Timex Classification #Timex Extraction #doi-10.57967/hf/1346 #region-us 
