Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
json
Size:
100K<n<1M
Tags:
Timex
Timexs
Temporal Expression
Temporal Expressions
Temporal Information
Timex Identification
License:
File size: 3,482 Bytes
512f6c9 2198d34 512f6c9 2198d34 b352145 2198d34 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
---
annotations_creators:
- machine-generated
language:
- en
- fr
- pt
- de
- fr
- it
- es
language_creators:
- found
license:
- mit
multilinguality:
- multilingual
pretty_name: Professor HeidelTime
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- Timex
- Timexs
- Temporal Expression
- Temporal Expressions
- Temporal Information
- Timex Identification
- Timex Classification
- Timex Extraction
task_categories:
- token-classification
task_ids:
- parsing
- part-of-speech
- named-entity-recognition
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"
---
# Professor HeidelTime
[Paper](https://dl.acm.org/doi/10.1145/3583780.3615130) [GitHub](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}
}
```
|