metadata

language:
  - it
language_details: it-IT
license: cc-by-nc-sa-4.0
task_categories:
  - text-classification
task_ids:
  - named-entity-recognition
size_categories:
  - 10K<n<100K
configs:
  - config_name: adg
    data_files:
      - split: train
        path: adg_train.jsonl
      - split: dev
        path: adg_dev.jsonl
      - split: test
        path: adg_test.jsonl
      - split: trial
        path: adg_trial.jsonl
      - split: reduced_test
        path: sampled_adg_test.jsonl
  - config_name: fic
    data_files:
      - split: train
        path: fic_train.jsonl
      - split: dev
        path: fic_dev.jsonl
      - split: test
        path: fic_test.jsonl
      - split: trial
        path: fic_trial.jsonl
      - split: reduced_test
        path: sampled_fic_test.jsonl
  - config_name: wn
    data_files:
      - split: train
        path: wn_train.jsonl
      - split: dev
        path: wn_dev.jsonl
      - split: test
        path: wn_test.jsonl
      - split: trial
        path: wn_trial.jsonl
      - split: reduced_test
        path: sampled_wn_test.jsonl

Data for the NERMuD shared task (Evalita 2023)

This data is the one used for the NERMuD shared task organized at Evalita 2023.

The dataset contains the Wikinews, fiction, and De Gasperi subsets of KIND, where test data is used for development.

Content of the dataset

Split	Sentences
wn_train	10,912
wn_dev	2,594
wn_test	2,088
fic_train	11,423
fic_dev	1,051
fic_test	1,517
adg_train	5,147
adg_dev	1,122
adg_test	521

Set	Sentences
Total train	27,482
Total dev	4,767
Total test	4,126
Total	36,380

Publications

@inproceedings{evalita2023nermud,
    title={{NERMuD} at {EVALITA} 2023: Overview of the Named-Entities Recognition on Multi-Domain Documents Task},
    author={Palmero Aprosio, Alessio and Paccosi, Teresa},
    booktitle={Proceedings of the Eighth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2023)},
    publisher = {CEUR.org},
    year = {2023},
    month = {September},
    address = {Parma, Italy}
}