metadata

license: cc-by-4.0
configs:
  - config_name: default
    data_files:
      - split: train
        path: estner-*/train/*
      - split: dev
        path: estner-*/dev/*
      - split: test
        path: estner-*/test/*
  - config_name: estner-reannotated
    data_files:
      - split: train
        path: estner-reannotated/train/*
      - split: dev
        path: estner-reannotated/dev/*
      - split: test
        path: estner-reannotated/test/*
  - config_name: estner-new
    data_files:
      - split: train
        path: estner-new/train/*
      - split: dev
        path: estner-new/dev/*
      - split: test
        path: estner-new/test/*
language:
  - et
pretty_name: EstNER
size_categories:
  - 10K<n<100K
task_categories:
  - token-classification

Dataset Card for EstNER

Dataset Description

EstNER dataset for named entity recogintion in Estonian language comprised of two parts: New EstNER and Reannotated EstNER (refer to the corresponding sections of this readme for additional details).

By default the joint version of the dataset is loaded.

from datasets import load_dataset

ds = load_dataset("tartuNLP/EstNER")

Each part can be loaded individually, as well.

from datasets import load_dataset

new_ds = load_dataset("tartuNLP/EstNER", "estner-new")
reannotated_ds = load_dataset("tartuNLP/EstNER", "estner-reannotated")

New Estonian NER dataset

The dataset is a collection of Estonian news and social media texts annotated with named entities.

Dataset statistics

The dataset is divided into training, development and test sets. The annotations can be hierarchical, meaning that there can be one named entity inside another. The maximum number of levels in the hierarchical annotations is three.

	Train	Dev	Test	Total
Documents	78	16	15	109
Sentences	7001	882	890	8773
Tokens	111858	13130	14686	139674
1-level entities	7480	497	938	8915
2-level entities	571	44	59	674
3-level entities	27	0	1	28

Annotated entities

The dataset is annotated with the following entities:

PER - person names
GPE - geopolitical entities
LOC - geographical locations
ORG - organizations
PROD - products, things, works of art
EVENT - events
DATE - dates
TIME - times
TITLE - titles and professions
MONEY - monetary expressions
PERCENT - percentages

Level 1 entities

	Train	Dev	Test	Total
PER	2601	109	299	3009
ORG	1177	85	150	1412
LOC	449	31	35	515
GPE	1253	129	231	1613
TITLE	702	19	59	772
PROD	624	60	117	801
EVENT	230	15	26	271
DATE	746	64	77	887
TIME	103	6	6	115
PERCENT	75	11	1	87
MONEY	118	12	1	131
Total	8078	541	994	9613

Level 2 entities

	Train	Dev	Test	Total
PER	108	1	14	123
ORG	92	8	6	106
LOC	25	1	0	26
GPE	379	35	42	456
TITLE	3	0	0	3
PROD	4	0	0	4
EVENT	1	0	0	1
DATE	10	0	0	10
TIME	0	0	0	0
PERCENT	0	0	0	0
MONEY	0	0	0	0
Total	622	45	62	729

Level 3 entities

	Train	Test	Total
PER	1	0	1
ORG	0	0	0
LOC	1	0	1
GPE	25	1	26
TITLE	0	0	0
PROD	0	0	0
EVENT	0	0	0
DATE	0	0	0
TIME	0	0	0
PERCENT	0	0	0
MONEY	0	0	0
Total	27	1	28

Reannotated Estonian NER dataset

This is the Estonian NER dataset (Tkachenko, 2010; Tkachenko et al., 2013) reannotated with a richer set of entities.

Dataset statistics

	Train	Dev	Test	Total
Documents	525	18	39	582
Sentences	9965	2415	1907	14287
Tokens	155983	32890	28370	217243
1-level entities	13918	2571	2396	18885
2-level entities	987	223	122	1332
3-level entities	40	14	4	58

Annotated entities

Originally, the Estonian NER dataset was annotated with PER, ORG and LOC entities only. The reannotated version is annotated with the following entities:

PER - person names
GPE - geopolitical entities
LOC - geographical locations
ORG - organizations
PROD - products, things, works of art
EVENT - events
DATE - dates
TIME - times
TITLE - titles and professions
MONEY - monetary expressions
PERCENT - percentages

Level 1 entities

	Train	Dev	Test	Total
PER	3563	642	722	4927
ORG	3215	504	541	4260
LOC	328	118	61	507
GPE	3377	714	479	4570
TITLE	1302	171	209	1682
PROD	874	161	66	1101
EVENT	56	13	17	86
DATE	1346	308	186	1840
TIME	456	39	30	525
PERCENT	137	62	58	257
MONEY	291	76	153	520
Total	14945	2808	2522	20275

Level 2 entities

	Train	Dev	Test	Total
PER	46	7	4	57
ORG	180	31	12	223
LOC	58	12	8	78
GPE	745	160	101	1006
TITLE	6	0	0	6
PROD	3	0	0	3
EVENT	5	0	0	5
DATE	7	34	1	42
TIME	0	0	0	0
PERCENT	1	0	0	1
MONEY	0	0	0	0
Total	1051	126	244	1421

Level 3 entities

	Train	Dev	Test	Total
PER	1	0	0	1
ORG	1	0	0	0
LOC	0	1	0	1
GPE	38	13	4	26
TITLE	0	0	0	0
PROD	0	0	0	0
EVENT	0	0	0	0
DATE	0	0	0	0
TIME	0	0	0	0
PERCENT	0	0	0	0
MONEY	0	0	0	0
Total	40	14	4	58

BibTeX entry and citation info

@inproceedings{sirts-2023-estonian,
    title = "{E}stonian Named Entity Recognition: New Datasets and Models",
    author = "Sirts, Kairit",
    editor = {Alum{\"a}e, Tanel  and
      Fishel, Mark},
    booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
    month = may,
    year = "2023",
    address = "T{\'o}rshavn, Faroe Islands",
    publisher = "University of Tartu Library",
    url = "https://aclanthology.org/2023.nodalida-1.76",
    pages = "752--761",
    abstract = "This paper presents the annotation process of two Estonian named entity recognition (NER) datasets, involving the creation of annotation guidelines for labeling eleven different types of entities. In addition to the commonly annotated entities such as person names, organization names, and locations, the annotation scheme encompasses geopolitical entities, product names, titles/roles, events, dates, times, monetary values, and percents. The annotation was performed on two datasets, one involving reannotating an existing NER dataset primarily composed of news texts and the other incorporating new texts from news and social media domains. Transformer-based models were trained on these annotated datasets to establish baseline predictive performance. Our findings indicate that the best results were achieved by training a single model on the combined dataset, suggesting that the domain differences between the datasets are relatively small.",
}