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README.md

@@ -1,3 +1,220 @@
 ---
-license: cc-by-nc-4.0
+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 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://ojs.aaai.org/index.php/AAAI/article/view/16938)
+- **Dataset:** NoisyNER
+- **Domain:** News
+
+### 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.

noisyner.py

@@ -0,0 +1,199 @@
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{hedderich2021analysing,
+  title={Analysing the Noise Model Error for Realistic Noisy Label Data},
+  author={Hedderich, Michael A and Zhu, Dawei and Klakow, Dietrich},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={35},
+  number={9},
+  pages={7675--7684},
+  year={2021}
+}
+@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",
+    year = "2013",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/W13-2412",
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+NoisyNER is a dataset for the evaluation of methods to handle noisy labels when training machine learning models. 
+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 our publication 
+https://ojs.aaai.org/index.php/AAAI/article/view/16938 (published at AAAI'21).
+"""
+
+_HOMEPAGE = "https://github.com/uds-lsv/NoisyNER"
+
+_LICENSE = "The original dataset is licensed under CC-BY-NC. We provide our noisy labels under CC-BY 4.0."
+
+_URL = "https://huggingface.co/datasets/phuctrg/noisyner/raw/main/data"
+
+
+class NoisyNER(datasets.GeneratorBasedBuilder):
+    """
+    NoisyNER is a dataset for the evaluation of methods to handle noisy labels when training machine learning models.
+    """
+
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="estner_clean", version=VERSION, description="EstNER dataset with clean labels"
+        ),
+        datasets.BuilderConfig(
+            name="NoisyNER_labelset1", version=VERSION,
+            description="NoisyNER dataset label set 1 "
+                        "with automatic annotation via distant supervision based ANEA tool with no heuristics"
+        ),
+        datasets.BuilderConfig(
+            name="NoisyNER_labelset2", version=VERSION,
+            description="NoisyNER dataset label set 2 "
+                        "with automatic annotation via distant supervision based ANEA tool and "
+                        "applying Estonian lemmatization to normalize the words"
+        ),
+        datasets.BuilderConfig(
+            name="NoisyNER_labelset3", version=VERSION,
+            description="NoisyNER dataset label set 3 "
+                        "with automatic annotation via distant supervision based ANEA tool and "
+                        "splitting person entity names in the list, i.e. both first and last names can be matched "
+                        "separately. Person names must have a minimum length of 4. Also, lemmatization"
+        ),
+        datasets.BuilderConfig(
+            name="NoisyNER_labelset4", version=VERSION,
+            description="NoisyNER dataset label set 4 "
+                        "with automatic annotation via distant supervision based ANEA tool and if entity names from "
+                        "two different lists match the same word, location entities are preferred. "
+                        "Also, lemmatization."
+        ),
+        datasets.BuilderConfig(
+            name="NoisyNER_labelset5", version=VERSION,
+            description="NoisyNER dataset label set 5 "
+                        "with automatic annotation via distant supervision based ANEA tool and "
+                        "Locations preferred, lemmatization, splitting names with minimum length 4."
+        ),
+        datasets.BuilderConfig(
+            name="NoisyNER_labelset6", version=VERSION,
+            description="NoisyNER dataset label set 6 "
+                        "with automatic annotation via distant supervision based ANEA tool and "
+                        "removing the entity names 'kohta', 'teine', 'naine' and 'mees' from the list of person names "
+                        "(high false positive rate). Also, all of label set 5."
+        ),
+        datasets.BuilderConfig(
+            name="NoisyNER_labelset7", version=VERSION,
+            description="NoisyNER dataset label set 7 "
+                        "with automatic annotation via distant supervision based ANEA tool and using alternative, "
+                        "alias names for organizations. Using additionally the identifiers Q82794, Q3957, Q7930989, "
+                        "Q5119 and Q11881845 for locations and Q1572070 and Q7278 for organizations. "
+                        "Also, all of label set 6."
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "estner_clean"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "tokens": datasets.Sequence(datasets.Value("string")),
+                "lemmas": datasets.Sequence(datasets.Value("string")),
+                "grammar": datasets.Sequence(datasets.Value("string")),
+                "ner_tags": datasets.Sequence(
+                    datasets.features.ClassLabel(
+                        names=[
+                            "O",
+                            "B-PER",
+                            "I-PER",
+                            "B-ORG",
+                            "I-ORG",
+                            "B-LOC",
+                            "I-LOC"
+                        ]
+                    )
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        _URLS = {
+            str(datasets.Split.TRAIN): f'{_URL}/{self.config.name}_train.tsv',
+            str(datasets.Split.VALIDATION): f'{_URL}/{self.config.name}_dev.tsv',
+            str(datasets.Split.TEST): f'{_URL}/{self.config.name}_test.tsv',
+        }
+
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)]})
+                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION, datasets.Split.TEST]]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            guid = 0
+            tokens = []
+            lemmas = []
+            grammar_infos = []
+            ner_tags = []
+            for line in f:
+                if line in ["--", "", "\n", "--\n"]:
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "lemmas": lemmas,
+                            "grammar": grammar_infos,
+                            "ner_tags": ner_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        lemmas = []
+                        grammar_infos = []
+                        ner_tags = []
+                else:
+                    splits = line.split("\t")
+                    tokens.append(splits[0])
+                    lemmas.append(splits[1])
+                    grammar_infos.append(splits[2])
+                    ner_tags.append(splits[3].rstrip())
+            # last example
+            if tokens:
+                yield guid, {
+                    "id": str(guid),
+                    "tokens": tokens,
+                    "lemmas": lemmas,
+                    "grammar": grammar_infos,
+                    "ner_tags": ner_tags,
+                }