metadata

annotations_creators:
  - expert-generated
language:
  - et
language_creators:
  - found
license:
  - cc-by-nc-4.0
multilinguality:
  - monolingual
paperswithcode_id: noisyner
pretty_name: NoisyNER
size_categories:
  - 10K<n<100K
source_datasets:
  - original
tags:
  - newspapers
  - 1997-2009
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition
dataset_info:
  - config_name: estner_clean
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: lemmas
        sequence: string
      - name: grammar
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
    splits:
      - name: train
        num_bytes: 7544221
        num_examples: 11365
      - name: validation
        num_bytes: 986310
        num_examples: 1480
      - name: test
        num_bytes: 995204
        num_examples: 1433
    download_size: 6258130
    dataset_size: 9525735
  - config_name: NoisyNER_labelset1
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: lemmas
        sequence: string
      - name: grammar
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
    splits:
      - name: train
        num_bytes: 7544221
        num_examples: 11365
      - name: validation
        num_bytes: 986310
        num_examples: 1480
      - name: test
        num_bytes: 995204
        num_examples: 1433
    download_size: 6194276
    dataset_size: 9525735
  - config_name: NoisyNER_labelset2
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: lemmas
        sequence: string
      - name: grammar
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
    splits:
      - name: train
        num_bytes: 7544221
        num_examples: 11365
      - name: validation
        num_bytes: 986310
        num_examples: 1480
      - name: test
        num_bytes: 995204
        num_examples: 1433
    download_size: 6201072
    dataset_size: 9525735
  - config_name: NoisyNER_labelset3
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: lemmas
        sequence: string
      - name: grammar
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
    splits:
      - name: train
        num_bytes: 7544221
        num_examples: 11365
      - name: validation
        num_bytes: 986310
        num_examples: 1480
      - name: test
        num_bytes: 995204
        num_examples: 1433
    download_size: 6231384
    dataset_size: 9525735
  - config_name: NoisyNER_labelset4
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: lemmas
        sequence: string
      - name: grammar
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
    splits:
      - name: train
        num_bytes: 7544221
        num_examples: 11365
      - name: validation
        num_bytes: 986310
        num_examples: 1480
      - name: test
        num_bytes: 995204
        num_examples: 1433
    download_size: 6201072
    dataset_size: 9525735
  - config_name: NoisyNER_labelset5
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: lemmas
        sequence: string
      - name: grammar
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
    splits:
      - name: train
        num_bytes: 7544221
        num_examples: 11365
      - name: validation
        num_bytes: 986310
        num_examples: 1480
      - name: test
        num_bytes: 995204
        num_examples: 1433
    download_size: 6231384
    dataset_size: 9525735
  - config_name: NoisyNER_labelset6
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: lemmas
        sequence: string
      - name: grammar
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
    splits:
      - name: train
        num_bytes: 7544221
        num_examples: 11365
      - name: validation
        num_bytes: 986310
        num_examples: 1480
      - name: test
        num_bytes: 995204
        num_examples: 1433
    download_size: 6226516
    dataset_size: 9525735
  - config_name: NoisyNER_labelset7
    features:
      - name: id
        dtype: string
      - name: tokens
        sequence: string
      - name: lemmas
        sequence: string
      - name: grammar
        sequence: string
      - name: ner_tags
        sequence:
          class_label:
            names:
              '0': O
              '1': B-PER
              '2': I-PER
              '3': B-ORG
              '4': I-ORG
              '5': B-LOC
              '6': I-LOC
    splits:
      - name: train
        num_bytes: 7544221
        num_examples: 11365
      - name: validation
        num_bytes: 986310
        num_examples: 1480
      - name: test
        num_bytes: 995204
        num_examples: 1433
    download_size: 6229668
    dataset_size: 9525735

Dataset Card for NoisyNER

Table of Contents
Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Repository: Estonian NER corpus, NoisyNER dataset
Paper: Named Entity Recognition in Estonian, Analysing the Noise Model Error for Realistic Noisy Label Data
Dataset: NoisyNER
Domain: News
Size of downloaded dataset files: 6.23 MB
Size of the generated dataset files: 9.53 MB

Dataset Summary

NoisyNER is a dataset for the evaluation of methods to handle noisy labels when training machine learning models.

Entity Types: PER, ORG, LOC

It is from the NLP/Information Extraction domain and was created through a realistic distant supervision technique. Some highlights and interesting aspects of the data are:

Seven sets of labels with differing noise patterns to evaluate different noise levels on the same instances
Full parallel clean labels available to compute upper performance bounds or study scenarios where a small amount of gold-standard data can be leveraged
Skewed label distribution (typical for Named Entity Recognition tasks)
For some label sets: noise level higher than the true label probability
Sequential dependencies between the labels

For more details on the dataset and its creation process, please refer to the original author's publication https://ojs.aaai.org/index.php/AAAI/article/view/16938 (published at AAAI'21).

This dataset is based on the Estonian NER corpus. For more details see https://aclanthology.org/W13-2412/

Supported Tasks and Leaderboards

More Information Needed

Languages

The language data in NoisyNER is in Estonian (BCP-47 et)

Dataset Structure

Data Instances

An example of 'train' looks as follows.

{
  'id': '0',
  'tokens': ['Tallinna', 'õhusaaste', 'suureneb', '.'],
  'lemmas': ['Tallinn+0', 'õhu_saaste+0', 'suurene+b', '.'],
  'grammar': ['_H_ sg g', '_S_ sg n', '_V_ b', '_Z_'],
  'ner_tags': [5, 0, 0, 0]
}

Data Fields

The data fields are the same among all splits.

id: a string feature.
tokens: a list of string features.
lemmas: a list of string features.
grammar: a list of string features.
ner_tags: a list of classification labels (int). Full tagset with indices:

{'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6}

Data Splits

The splits are the same across all configurations.

train	validation	test
11365	1480	1433

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

Tkachenko et al (2013) collected 572 news stories published in the local online newspapers Delfi and Postimees between 1997 and 2009. Selected articles cover both local and international news on a range of topics including politics, economics and sports. The raw text was preprocessed using the morphological disambiguator t3mesta (Kaalep and Vaino, 1998) provided by Filosoft. The processing steps involve tokenization, lemmatization, part-of-speech tagging, grammatical and morphological analysis.

Who are the source language producers?

More Information Needed

Annotations

Annotation process

According to Tkachenko et al (2013) one of the authors manually tagged the corpus and the other author examined the tags, after which conflicting cases were resolved. The total size of the corpus is 184,638 tokens. Tkachenko et al (2013) provide the following number of named entities in the corpus:

	PER	LOC	ORG	Total
All	5762	5711	3938	15411
Unique	3588	1589	1987	7164

Hedderich et al (2021) obtained the noisy labels through a distant supervision/automatic annotation approach. They extracted lists of named entities from Wikidata and matched them against words in the text via the ANEA tool (Hedderich, Lange, and Klakow 2021). They also used heuristic functions to correct errors caused by non-complete lists of entities, grammatical complexities of Estonian that do not allow simple string matching or entity lists in conflict with each other. For instance, they normalized the grammatical form of a word or excluded certain high false-positive words. They provide seven sets of labels that differ in the noise process. This results in 8 different configurations, when added to the original split with clean labels.

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information

@inproceedings{tkachenko-etal-2013-named,
    title = "Named Entity Recognition in {E}stonian",
    author = "Tkachenko, Alexander  and
      Petmanson, Timo  and
      Laur, Sven",
    booktitle = "Proceedings of the 4th Biennial International Workshop on {B}alto-{S}lavic Natural Language Processing",
    month = aug,
    year = "2013",
    address = "Sofia, Bulgaria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W13-2412",
    pages = "78--83",
}
@article{Hedderich_Zhu_Klakow_2021, 
    title={Analysing the Noise Model Error for Realistic Noisy Label Data}, 
    author={Hedderich, Michael A. and Zhu, Dawei and Klakow, Dietrich},  
    volume={35}, 
    url={https://ojs.aaai.org/index.php/AAAI/article/view/16938}, 
    number={9}, 
    journal={Proceedings of the AAAI Conference on Artificial Intelligence},
    year={2021}, 
    month={May}, 
    pages={7675-7684}, 
}

Contributions

Thanks to @phucdev for adding this dataset.

Dataset Card for NoisyNER

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions