Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
named-entity-recognition
Languages:
Estonian
Size:
100K - 1M
ArXiv:
License:
| 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 | |
| - [Table of Contents](#table-of-contents) | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Repository:** [Estonian NER corpus](https://doi.org/10.15155/1-00-0000-0000-0000-00073L), [NoisyNER dataset](https://github.com/uds-lsv/NoisyNER) | |
| - **Paper:** [Named Entity Recognition in Estonian](https://aclanthology.org/W13-2412/), [Analysing the Noise Model Error for Realistic Noisy Label Data](https://arxiv.org/abs/2101.09763) | |
| - **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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### 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: | |
| ```python | |
| {'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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| Tkachenko et al (2013) collected 572 news stories published in the local online newspapers [Delfi](http://delfi.ee/) and [Postimees](http://postimees.ee/) 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](https://www.cl.ut.ee/yllitised/kk_yhest_1998.pdf)) provided by [Filosoft](http://www.filosoft.ee/). The processing steps involve tokenization, lemmatization, part-of-speech tagging, grammatical and morphological analysis. | |
| #### Who are the source language producers? | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### 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](https://arxiv.org/abs/2102.13129)). 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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Personal and Sensitive Information | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Discussion of Biases | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Other Known Limitations | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ## Additional Information | |
| ### Dataset Curators | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### Licensing Information | |
| [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | |
| ### 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](https://github.com/phucdev) for adding this dataset. |