metadata

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 GitHub

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:

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 at hugo.o.sousa@inesctec.pt.

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:

@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}
}