Datasets:

GateNLP
/

broad_twitter_corpus

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----
-annotations_creators:
-- crowdsourced
-language_creators:
-- found
-language:
-- en
-license:
-- cc-by-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 100K<n<1M
-source_datasets:
-- original
-task_categories:
-- token-classification
-task_ids:
-- named-entity-recognition
-paperswithcode_id: broad-twitter-corpus
-pretty_name: Broad Twitter Corpus
----
-# Dataset Card for broad_twitter_corpus
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-instances)
-  - [Data Splits](#data-instances)
-- [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)
-## Dataset Description
-- **Homepage:** [https://github.com/GateNLP/broad_twitter_corpus](https://github.com/GateNLP/broad_twitter_corpus)
-- **Repository:** [https://github.com/GateNLP/broad_twitter_corpus](https://github.com/GateNLP/broad_twitter_corpus)
-- **Paper:** [http://www.aclweb.org/anthology/C16-1111](http://www.aclweb.org/anthology/C16-1111)
-- **Leaderboard:** [Named Entity Recognition on Broad Twitter Corpus](https://paperswithcode.com/sota/named-entity-recognition-on-broad-twitter)
-- **Point of Contact:** [Leon Derczynski](https://github.com/leondz)
-### Dataset Summary
-This is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses. The goal is to represent a broad range of activities, giving a dataset more representative of the language used in this hardest of social media formats to process. Further, the BTC is annotated for named entities.
-See the paper, [Broad Twitter Corpus: A Diverse Named Entity Recognition Resource](http://www.aclweb.org/anthology/C16-1111), for details.
-### Supported Tasks and Leaderboards
-* Named Entity Recognition
-* On PWC: [Named Entity Recognition on Broad Twitter Corpus](https://paperswithcode.com/sota/named-entity-recognition-on-broad-twitter)
-### Languages
-English from UK, US, Australia, Canada, Ireland, New Zealand; `bcp47:en`
-## Dataset Structure
-### Data Instances
-Feature |Count
----|---:
-Documents |9 551
-Tokens |165 739
-Person entities |5 271
-Location entities |3 114
-Organization entities |3 732
-### Data Fields
-Each tweet contains an ID, a list of tokens, and a list of NER tags
-- `id`: a `string` feature.
-- `tokens`: a `list` of `strings`
-- `ner_tags`: a `list` of class IDs (`int`s) representing the NER class:
-```
-  0: O
-  1: B-PER
-  2: I-PER
-  3: B-ORG
-  4: I-ORG
-  5: B-LOC
-  6: I-LOC
-```
-### Data Splits
-Section|Region|Collection period|Description|Annotators|Tweet count
----|---|---|---|---|---:
-A | UK| 2012.01| General collection |Expert| 1000
-B |UK |2012.01-02 |Non-directed tweets |Expert |2000
-E |Global| 2014.07| Related to MH17 disaster| Crowd & expert |200
-F |Stratified |2009-2014| Twitterati |Crowd & expert |2000
-G |Stratified| 2011-2014| Mainstream news| Crowd & expert| 2351
-H |Non-UK| 2014 |General collection |Crowd & expert |2000
-The most varied parts of the BTC are sections F and H. However, each of the remaining four sections has some specific readily-identifiable bias. So, we propose that one uses half of section H for evaluation and leaves the other half in the training data. Section H should be partitioned in the order of the JSON-format lines. Note that the CoNLL-format data is readily reconstructible from the JSON format, which is the authoritative data format from which others are derived.
-**Test**: Section F
-**Development**: Section H (the paper says "second half of Section H" but ordinality could be ambiguous, so it all goes in. Bonne chance)
-**Training**: everything else
-## Dataset Creation
-### Curation Rationale
-[Needs More Information]
-### Source Data
-#### Initial Data Collection and Normalization
-[Needs More Information]
-#### Who are the source language producers?
-[Needs More Information]
-### Annotations
-#### Annotation process
-[Needs More Information]
-#### Who are the annotators?
-[Needs More Information]
-### Personal and Sensitive Information
-[Needs More Information]
-## Considerations for Using the Data
-### Social Impact of Dataset
-[Needs More Information]
-### Discussion of Biases
-[Needs More Information]
-### Other Known Limitations
-[Needs More Information]
-## Additional Information
-### Dataset Curators
-[Needs More Information]
-### Licensing Information
-Creative Commons Attribution 4.0 International (CC BY 4.0)
-### Citation Information
-```
-@inproceedings{derczynski2016broad,
-  title={Broad twitter corpus: A diverse named entity recognition resource},
-  author={Derczynski, Leon and Bontcheva, Kalina and Roberts, Ian},
-  booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
-  pages={1169--1179},
-  year={2016}
-}
-```
-### Contributions
-Author-added dataset [@leondz](https://github.com/leondz)

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broad_twitter_corpus.py DELETED Viewed

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-# coding=utf-8
-# Copyright 2020 HuggingFace Datasets Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# Lint as: python3
-"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition"""
-import os
-import datasets
-logger = datasets.logging.get_logger(__name__)
-_CITATION = """\
-@inproceedings{derczynski2016broad,
-  title={Broad twitter corpus: A diverse named entity recognition resource},
-  author={Derczynski, Leon and Bontcheva, Kalina and Roberts, Ian},
-  booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
-  pages={1169--1179},
-  year={2016}
-}
-"""
-_DESCRIPTION = """\
-This is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses.
-The goal is to represent a broad range of activities, giving a dataset more representative of the language used
-in this hardest of social media formats to process. Further, the BTC is annotated for named entities.
-For more details see [https://aclanthology.org/C16-1111/](https://aclanthology.org/C16-1111/)
-"""
-_URL = "https://github.com/GateNLP/broad_twitter_corpus/archive/refs/heads/master.zip"
-_subpath = "broad_twitter_corpus-master/"
-_A_FILE = _subpath + "a.conll"
-_B_FILE = _subpath + "b.conll"
-_E_FILE = _subpath + "e.conll"
-_F_FILE = _subpath + "f.conll"
-_G_FILE = _subpath + "g.conll"
-_H_FILE = _subpath + "h.conll"
-# _TRAINING_FILE = "train.txt"
-_DEV_FILE = _H_FILE
-_TEST_FILE = _F_FILE
-class BroadTwitterCorpusConfig(datasets.BuilderConfig):
-    """BuilderConfig for BroadTwitterCorpus"""
-    def __init__(self, **kwargs):
-        """BuilderConfig for BroadTwitterCorpus.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(BroadTwitterCorpusConfig, self).__init__(**kwargs)
-class BroadTwitterCorpus(datasets.GeneratorBasedBuilder):
-    """BroadTwitterCorpus dataset."""
-    BUILDER_CONFIGS = [
-        BroadTwitterCorpusConfig(name="broad-twitter-corpus", version=datasets.Version("1.0.0"), description="Broad Twitter Corpus"),
-    ]
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "tokens": 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",
-                            ]
-                        )
-                    ),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://aclanthology.org/C16-1111/",
-            citation=_CITATION,
-        )
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        downloaded_file = dl_manager.download_and_extract(_URL)
-        data_files = {
-            "a": os.path.join(downloaded_file, _A_FILE),
-            "b": os.path.join(downloaded_file, _B_FILE),
-            "e": os.path.join(downloaded_file, _E_FILE),
-            "f": os.path.join(downloaded_file, _F_FILE),
-            "g": os.path.join(downloaded_file, _G_FILE),
-            "h": os.path.join(downloaded_file, _H_FILE),
-            "dev": os.path.join(downloaded_file, _DEV_FILE),
-            "test": os.path.join(downloaded_file, _TEST_FILE),
-        }
-        """
-        btc_section_a = datasets.SplitGenerator(name="BTC_A", gen_kwargs={"filepath": data_files["a"]})
-        btc_section_b = datasets.SplitGenerator(name="BTC_B", gen_kwargs={"filepath": data_files["b"]})
-        btc_section_e = datasets.SplitGenerator(name="BTC_E", gen_kwargs={"filepath": data_files["e"]})
-        btc_section_f = datasets.SplitGenerator(name="BTC_F", gen_kwargs={"filepath": data_files["f"]})
-        btc_section_g = datasets.SplitGenerator(name="BTC_G", gen_kwargs={"filepath": data_files["g"]})
-        btc_section_h = datasets.SplitGenerator(name="BTC_H", gen_kwargs={"filepath": data_files["h"]})
-        """
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN,
-                gen_kwargs={"filepaths": [data_files['a'], data_files['b'], data_files['e'], data_files['g']]}
-                ),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": [data_files["dev"]]}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": [data_files["test"]]}),
-        ]
-    def _generate_examples(self, filepaths):
-        guid = 0
-        for filepath in filepaths:
-            with open(filepath, encoding="utf-8") as f:
-                logger.info("⏳ Generating examples from = %s", filepath)
-                tokens = []
-                ner_tags = []
-                for line in f:
-                    if line.startswith("-DOCSTART-") or line.strip() == "" or line == "\n":
-                        if tokens:
-                            yield guid, {
-                                "id": str(guid),
-                                "tokens": tokens,
-                                "ner_tags": ner_tags,
-                            }
-                            guid += 1
-                            tokens = []
-                            ner_tags = []
-                    else:
-                        # btc entries are tab separated
-                        fields = line.split("\t")
-                        tokens.append(fields[0])
-                        ner_tags.append(fields[1].rstrip())
-                # last example
-                yield guid, {
-                    "id": str(guid),
-                    "tokens": tokens,
-                    "ner_tags": ner_tags,
-                }
-                guid += 1 # for when files roll over

dataset_infos.json DELETED Viewed

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- {"broad-twitter-corpus": {"description": "This is the Broad Twitter corpus, a dataset of tweets collected over stratified times, places and social uses. \nThe goal is to represent a broad range of activities, giving a dataset more representative of the language used \nin this hardest of social media formats to process. Further, the BTC is annotated for named entities.\n\nFor more details see [https://aclanthology.org/C16-1111/](https://aclanthology.org/C16-1111/)\n", "citation": "@inproceedings{derczynski2016broad,\n title={Broad twitter corpus: A diverse named entity recognition resource},\n author={Derczynski, Leon and Bontcheva, Kalina and Roberts, Ian},\n booktitle={Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},\n pages={1169--1179},\n year={2016}\n}\n", "homepage": "https://aclanthology.org/C16-1111/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 7, "names": ["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"], "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "broad_twitter_corpus", "config_name": "broad-twitter-corpus", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1522066, "num_examples": 5342, "dataset_name": "broad_twitter_corpus"}, "validation": {"name": "validation", "num_bytes": 514159, "num_examples": 2002, "dataset_name": "broad_twitter_corpus"}, "test": {"name": "test", "num_bytes": 621542, "num_examples": 2002, "dataset_name": "broad_twitter_corpus"}}, "download_checksums": {"https://github.com/GateNLP/broad_twitter_corpus/archive/refs/heads/master.zip": {"num_bytes": 39344594, "checksum": "4e1d1a7d0d9e7563f5df2adf078c25a7305ab6a5e74eadae123881e4d175a12f"}}, "download_size": 39344594, "post_processing_size": null, "dataset_size": 2657767, "size_in_bytes": 42002361}}

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