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ginco

Tasks:
Text Classification
Languages:
Slovenian
Size:
1K<n<10K
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metadata
dataset_info:
  features:
    - name: id
      dtype: string
    - name: url
      dtype: string
    - name: crawled
      dtype: string
    - name: hard
      dtype: bool
    - name: paragraphs
      list:
        - name: text
          dtype: string
        - name: duplicate
          dtype: bool
        - name: keep
          dtype: bool
    - name: primary_level_1
      dtype: string
    - name: primary_level_2
      dtype: string
    - name: primary_level_3
      dtype: string
    - name: secondary_level_1
      dtype: string
    - name: secondary_level_2
      dtype: string
    - name: secondary_level_3
      dtype: string
    - name: tertiary_level_1
      dtype: string
    - name: tertiary_level_2
      dtype: string
    - name: tertiary_level_3
      dtype: string
    - name: split
      dtype: string
    - name: domain
      dtype: string
  splits:
    - name: train
      num_bytes: 2250345
      num_examples: 602
    - name: validation
      num_bytes: 657986
      num_examples: 200
    - name: test
      num_bytes: 550742
      num_examples: 200
  download_size: 1424443
  dataset_size: 3459073
license:
  - cc-by-sa-4.0
language:
  - sl
multilinguality:
  - monolingual
task_categories:
  - text-classification
size_categories:
  - 1K<n<10K

Dataset Card for Ginco

Dataset Summary

The Slovene Web genre identification corpus GINCO 1.0 contains 1,002 web texts (478,969 words), manually annotated with genres. The corpus allows for automated genre identification and genre analyses as well as other web corpora research.

This dataset was extracted from the manually-annotated subcorpus (GINCO-1.0-suitable.json.zip) from the original GINCO dataset, published on the CLARIN.SI repository.

The dataset is split into 602 training, 200 validation, and 200 test texts by the original authors.

The texts in the suitable subset are annotated with up to three genre categories, where the primary label is the most prevalent, and secondary and tertiary labels denote presence of additional genre(s). The secondary and tertiary labels are available for multilabel classification, while for most use cases, we suggest that only the primary label is used.

The labels are provided in three levels of detail (three category sets), allowing experiments with the full set (24 labels), set of 21 labels (labels with less than 5 instances are merged with label Other) and set of 12 labels (similar labels are merged). For most use cases, we suggest that the smallest set -- set of 12 labels is used (primary_level_3), and that the category "Other" is regarded as a "throw-away" category to detect texts for which the classifier could not predict any of the concrete labels.

Additionally, the corpus contains some metadata about the text (e.g. url, domain, year) and its paragraphs (e.g. near-duplicates and their usefulness for the genre identification).

More details on dataset construction, manual annotation and results of machine learning experiments are provided in the paper "The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild" (Kuzman et al., 2022).

Languages

Slovenian.

Dataset Structure

Data Instances

A sample instance from the dataset:

{
    "id": "3776",
    "url": "http://www.radiocelje.si/novica.php?id=13007&amp;m=11&amp;l=2010",
    "crawled": "2014",
    "hard": false,
    "paragraphs": [
        {
            "text": "V novembru, mesecu prepre\u010devanja odvisnosti, bodo \u010dlani Lokalne akcijske skupine za prepre\u010devanje zasvojenosti izvedli niz strokovnih predavanj za star\u0161e osnovno\u0161olcev v Celju...",
            "duplicate": false,
            "keep": true
        },
        {
            "text": "Predavanja, ki jih bodo ta mesec organizirali na devetih osnovnih \u0161olah v mestni ob\u010dini Celje, so namenjena star\u0161em u\u010dencev od \u0161estega do devetega razreda. Program predavanj finan\u010dno podpira Mestna ob\u010dina Celje. Osrednja tema predavanj bodo varovalni dejavniki vzgoje, ki lahko pripomorejo k neuporabi drog. Po drogah, dovoljenih in nedovoljenih namre\u010d vse pogosteje posegajo \u017ee otroci. Na predavanjih se bodo star\u0161i seznanili tudi z informacijami o tem, na katere vedenjske in telesne spremembe naj bodo pozorni, kadar sumijo, da je otrok posegel po drogi. \u010ceprav je tema aktualna, saj poleg problemov odvisnosti osvetljuje ve\u0161\u010dine u\u010dinkovitega star\u0161evstva in komuniciranja z otroki v konfliktnih situacijah, se je lani ciklusa predavanj na osmih osnovnih \u0161olah udele\u017eilo le 160 star\u0161ev. Organizatorji tokrat upajo na bolj\u0161i odziv. Niz predavanj bodo izvedli strokovnjaki s podro\u010dja medicine, psihologije, socialnega dela in kriminologije. (ba)",
            "duplicate": false,
            "keep": true
        }
    ],
    "primary_level_1": "News/Reporting",
    "primary_level_2": "News/Reporting",
    "primary_level_3": "News/Reporting",
    "secondary_level_1": "",
    "secondary_level_2": "",
    "secondary_level_3": "",
    "tertiary_level_1": "",
    "tertiary_level_2": "",
    "tertiary_level_3": "",
    "split": "train",
    "domain": "www.radiocelje.si"
}

Data Fields

  • 'id': id of the example;
  • 'url': exact URL from where the text originates;
  • 'crawled': the year, when the text has been obtained from the stated URL;
  • 'hard': whether it was difficult for a human to assign a genre to the text;
  • 'paragraphs':
    • 'text': text of the paragraph;
    • 'duplicate': true if the text is a near-duplicate;
    • 'keep': true, if the text is useful for the genre identification, false, if not;
  • 'primary_level_1': first genre category, most detailed category set;
  • 'primary_level_2': first genre category, category set where too infrequent categories are merged to Other;
  • 'primary_level_3': first genre category, compact and most useful category set;
  • 'secondary_level_1': second genre category, most detailed category set;
  • 'secondary_level_2': second genre category, category set where too infrequent categories are merged to Other;
  • 'secondary_level_3': second genre category, compact and most useful category set;
  • 'tertiary_level_1': third genre category, most detailed category set;
  • 'tertiary_level_2': third genre category, category set where too infrequent categories are merged to Other;
  • 'tertiary_level_3': third genre category, compact and most useful category set;
  • 'split': example can belong to the 'train', 'dev', or 'test' split;
  • 'domain': domain address of the website where the text originates from.

Genre categories

Texts are annotated using 24 genre categories:

  • News/Reporting,
  • Announcement,
  • Research Article,
  • Instruction,
  • Recipe,
  • Call (such as a Call for Papers),
  • Legal/Regulation,
  • Information/Explanation,
  • Opinionated News,
  • Review,
  • Opinion/Argumentation,
  • Promotion of a Product,
  • Promotion of Services,
  • Invitation,
  • Promotion,
  • Interview,
  • Forum,
  • Correspondence,
  • Script/Drama,
  • Prose,
  • Lyrical,
  • FAQ (Frequently Asked Questions),
  • List of Summaries/Excerpts,
  • Other.

See the Appendix in the paper for descriptions of the labels.

Additional Information

Dataset Curators

Kuzman, Taja ; Brglez, Mojca ; Rupnik, Peter and Ljubešić, Nikola

Licensing Information

CC BY-SA 4.0

Citation Information

To cite the dataset:

@misc{11356/1467,
 title = {Slovene Web genre identification corpus {GINCO} 1.0},
 author = {Kuzman, Taja and Brglez, Mojca and Rupnik, Peter and Ljube{\v s}i{\'c}, Nikola},
 url = {http://hdl.handle.net/11356/1467},
 note = {Slovenian language resource repository {CLARIN}.{SI}},
 copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
 issn = {2820-4042},
 year = {2021}
}

To cite the paper on the dataset construction and manual annotation:

@inproceedings{kuzman2022ginco,
  title={The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild},
  author={Kuzman, Taja and Rupnik, Peter and Ljube{\v{s}}i{\'c}, Nikola},
  booktitle={Proceedings of the Thirteenth Language Resources and Evaluation Conference},
  pages={1584--1594},
  year={2022}
}

Contributions

Thanks to Hana Skitek for adding this dataset, and Taja Kuzman for extending the readme with additional information on the dataset.