Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Languages:
Turkish
Size:
100K<n<1M
ArXiv:
License:
Commit
•
8e3b9cf
0
Parent(s):
Update files from the datasets library (from 1.2.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.2.0
- .gitattributes +27 -0
- README.md +155 -0
- dataset_infos.json +1 -0
- dummy/0.0.0/dummy_data.zip +3 -0
- turkish_ner.py +170 -0
.gitattributes
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README.md
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---
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annotations_creators:
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- machine-generated
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language_creators:
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- expert-generated
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languages:
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- tr
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licenses:
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- cc-by-4-0
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multilinguality:
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- monolingual
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size_categories:
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- 100K<n<1M
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source_datasets:
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- original
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task_categories:
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- structure-prediction
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task_ids:
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- named-entity-recognition
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---
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# Dataset Card for turkish_ner
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-instances)
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- [Data Splits](#data-instances)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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## Dataset Description
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- **Homepage:** http://arxiv.org/abs/1702.02363
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- **Repository:** [Needs More Information]
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- **Paper:** http://arxiv.org/abs/1702.02363
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- **Leaderboard:** [Needs More Information]
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- **Point of Contact:** erayyildiz@ktu.edu.tr
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### Dataset Summary
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Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.
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### Supported Tasks and Leaderboards
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[Needs More Information]
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### Languages
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Turkish
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## Dataset Structure
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### Data Instances
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[More Information Needed]
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### Data Fields
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[More Information Needed]
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### Data Splits
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There's only the training set.
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## Dataset Creation
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### Curation Rationale
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[More Information Needed]
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### Source Data
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#### Initial Data Collection and Normalization
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[More Information Needed]
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#### Who are the source language producers?
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[More Information Needed]
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### Annotations
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#### Annotation process
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[More Information Needed]
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#### Who are the annotators?
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[More Information Needed]
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### Personal and Sensitive Information
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[More Information Needed]
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## Considerations for Using the Data
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### Social Impact of Dataset
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[More Information Needed]
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### Discussion of Biases
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[More Information Needed]
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### Other Known Limitations
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[More Information Needed]
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## Additional Information
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### Dataset Curators
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H. Bahadir Sahin, Caglar Tirkaz, Eray Yildiz, Mustafa Tolga Eren and Omer Ozan Sonmez
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### Licensing Information
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Creative Commons Attribution 4.0 International
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### Citation Information
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@InProceedings@article{DBLP:journals/corr/SahinTYES17,
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author = {H. Bahadir Sahin and
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Caglar Tirkaz and
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Eray Yildiz and
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Mustafa Tolga Eren and
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Omer Ozan Sonmez},
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title = {Automatically Annotated Turkish Corpus for Named Entity Recognition
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and Text Categorization using Large-Scale Gazetteers},
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journal = {CoRR},
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volume = {abs/1702.02363},
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year = {2017},
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url = {http://arxiv.org/abs/1702.02363},
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archivePrefix = {arXiv},
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eprint = {1702.02363},
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timestamp = {Mon, 13 Aug 2018 16:46:36 +0200},
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biburl = {https://dblp.org/rec/journals/corr/SahinTYES17.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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dataset_infos.json
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{"default": {"description": "Turkish Wikipedia Named-Entity Recognition and Text Categorization\n(TWNERTC) dataset is a collection of automatically categorized and annotated\nsentences obtained from Wikipedia. The authors constructed large-scale\ngazetteers by using a graph crawler algorithm to extract\nrelevant entity and domain information\nfrom a semantic knowledge base, Freebase.\nThe constructed gazetteers contains approximately\n300K entities with thousands of fine-grained entity types\nunder 77 different domains.\n", "citation": "@InProceedings@article{DBLP:journals/corr/SahinTYES17,\n author = {H. Bahadir Sahin and\n Caglar Tirkaz and\n Eray Yildiz and\n Mustafa Tolga Eren and\n Omer Ozan Sonmez},\n title = {Automatically Annotated Turkish Corpus for Named Entity Recognition\n and Text Categorization using Large-Scale Gazetteers},\n journal = {CoRR},\n volume = {abs/1702.02363},\n year = {2017},\n url = {http://arxiv.org/abs/1702.02363},\n archivePrefix = {arXiv},\n eprint = {1702.02363},\n timestamp = {Mon, 13 Aug 2018 16:46:36 +0200},\n biburl = {https://dblp.org/rec/journals/corr/SahinTYES17.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://data.mendeley.com/datasets/cdcztymf4k/1", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "domain": {"num_classes": 25, "names": ["architecture", "basketball", "book", "business", "education", "fictional_universe", "film", "food", "geography", "government", "law", "location", "military", "music", "opera", "organization", "people", "religion", "royalty", "soccer", "sports", "theater", "time", "travel", "tv"], "names_file": null, "id": null, "_type": "ClassLabel"}, "ner_tags": {"feature": {"num_classes": 9, "names": ["O", "B-PERSON", "I-PERSON", "B-ORGANIZATION", "I-ORGANIZATION", "B-LOCATION", "I-LOCATION", "B-MISC", "I-MISC"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "turkish_ner", "config_name": "default", "version": {"version_str": "0.0.0", "description": null, "major": 0, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 177658278, "num_examples": 532629, "dataset_name": "turkish_ner"}}, "download_checksums": {"https://data.mendeley.com/public-files/datasets/cdcztymf4k/files/5557ef78-7d53-4a01-8241-3173c47bbe10/file_downloaded": {"num_bytes": 204393976, "checksum": "e03e2867a225d63f0139dd4ced028e5da795a8a48e140ad4c17999a8560dbc57"}}, "download_size": 204393976, "post_processing_size": null, "dataset_size": 177658278, "size_in_bytes": 382052254}}
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dummy/0.0.0/dummy_data.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:7ce14f1ea12dda073ba35fe0381e477c03c176f8c4d192d87b72977846b89c9d
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size 5801
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turkish_ner.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""TODO: Add a description here."""
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from __future__ import absolute_import, division, print_function
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import logging
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import os
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import datasets
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# TODO: Add BibTeX citation
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# Find for instance the citation on arxiv or on the dataset repo/website
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_CITATION = """\
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@InProceedings@article{DBLP:journals/corr/SahinTYES17,
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author = {H. Bahadir Sahin and
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Caglar Tirkaz and
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Eray Yildiz and
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Mustafa Tolga Eren and
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Omer Ozan Sonmez},
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title = {Automatically Annotated Turkish Corpus for Named Entity Recognition
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and Text Categorization using Large-Scale Gazetteers},
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journal = {CoRR},
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volume = {abs/1702.02363},
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year = {2017},
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url = {http://arxiv.org/abs/1702.02363},
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archivePrefix = {arXiv},
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eprint = {1702.02363},
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timestamp = {Mon, 13 Aug 2018 16:46:36 +0200},
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biburl = {https://dblp.org/rec/journals/corr/SahinTYES17.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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"""
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# TODO: Add description of the dataset here
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# You can copy an official description
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_DESCRIPTION = """\
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Turkish Wikipedia Named-Entity Recognition and Text Categorization
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(TWNERTC) dataset is a collection of automatically categorized and annotated
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sentences obtained from Wikipedia. The authors constructed large-scale
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gazetteers by using a graph crawler algorithm to extract
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55 |
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relevant entity and domain information
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from a semantic knowledge base, Freebase.
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57 |
+
The constructed gazetteers contains approximately
|
58 |
+
300K entities with thousands of fine-grained entity types
|
59 |
+
under 77 different domains.
|
60 |
+
"""
|
61 |
+
|
62 |
+
# TODO: Add a link to an official homepage for the dataset here
|
63 |
+
_HOMEPAGE = "https://data.mendeley.com/datasets/cdcztymf4k/1"
|
64 |
+
|
65 |
+
# TODO: Add the licence for the dataset here if you can find it
|
66 |
+
_LICENSE = "Creative Commons Attribution 4.0 International"
|
67 |
+
|
68 |
+
_URL = "https://data.mendeley.com/public-files/datasets/cdcztymf4k/files/5557ef78-7d53-4a01-8241-3173c47bbe10/file_downloaded"
|
69 |
+
|
70 |
+
|
71 |
+
_FILE_NAME_ZIP = "TWNERTC_TC_Coarse Grained NER_DomainIndependent_NoiseReduction.zip"
|
72 |
+
_FILE_NAME = "TWNERTC_TC_Coarse Grained NER_DomainIndependent_NoiseReduction.DUMP"
|
73 |
+
|
74 |
+
|
75 |
+
class TurkishNER(datasets.GeneratorBasedBuilder):
|
76 |
+
"""TODO: Short description of my dataset."""
|
77 |
+
|
78 |
+
def _info(self):
|
79 |
+
return datasets.DatasetInfo(
|
80 |
+
description=_DESCRIPTION,
|
81 |
+
features=datasets.Features(
|
82 |
+
{
|
83 |
+
"id": datasets.Value("string"),
|
84 |
+
"tokens": datasets.Sequence(datasets.Value("string")),
|
85 |
+
"domain": datasets.ClassLabel(
|
86 |
+
names=[
|
87 |
+
"architecture",
|
88 |
+
"basketball",
|
89 |
+
"book",
|
90 |
+
"business",
|
91 |
+
"education",
|
92 |
+
"fictional_universe",
|
93 |
+
"film",
|
94 |
+
"food",
|
95 |
+
"geography",
|
96 |
+
"government",
|
97 |
+
"law",
|
98 |
+
"location",
|
99 |
+
"military",
|
100 |
+
"music",
|
101 |
+
"opera",
|
102 |
+
"organization",
|
103 |
+
"people",
|
104 |
+
"religion",
|
105 |
+
"royalty",
|
106 |
+
"soccer",
|
107 |
+
"sports",
|
108 |
+
"theater",
|
109 |
+
"time",
|
110 |
+
"travel",
|
111 |
+
"tv",
|
112 |
+
]
|
113 |
+
),
|
114 |
+
"ner_tags": datasets.Sequence(
|
115 |
+
datasets.features.ClassLabel(
|
116 |
+
names=[
|
117 |
+
"O",
|
118 |
+
"B-PERSON",
|
119 |
+
"I-PERSON",
|
120 |
+
"B-ORGANIZATION",
|
121 |
+
"I-ORGANIZATION",
|
122 |
+
"B-LOCATION",
|
123 |
+
"I-LOCATION",
|
124 |
+
"B-MISC",
|
125 |
+
"I-MISC",
|
126 |
+
]
|
127 |
+
)
|
128 |
+
),
|
129 |
+
}
|
130 |
+
),
|
131 |
+
supervised_keys=None,
|
132 |
+
# Homepage of the dataset for documentation
|
133 |
+
homepage=_HOMEPAGE,
|
134 |
+
# License for the dataset if available
|
135 |
+
license=_LICENSE,
|
136 |
+
# Citation for the dataset
|
137 |
+
citation=_CITATION,
|
138 |
+
)
|
139 |
+
|
140 |
+
def _split_generators(self, dl_manager):
|
141 |
+
"""Returns SplitGenerators."""
|
142 |
+
data_dir = dl_manager.extract(os.path.join(dl_manager.download_and_extract(_URL), _FILE_NAME_ZIP))
|
143 |
+
return [
|
144 |
+
datasets.SplitGenerator(
|
145 |
+
name=datasets.Split.TRAIN,
|
146 |
+
gen_kwargs={
|
147 |
+
"filepath": (os.path.join(data_dir, _FILE_NAME)),
|
148 |
+
"split": "train",
|
149 |
+
},
|
150 |
+
),
|
151 |
+
]
|
152 |
+
|
153 |
+
def _generate_examples(self, filepath, split):
|
154 |
+
""" Yields examples. """
|
155 |
+
logging.info("⏳ Generating examples from = %s", filepath)
|
156 |
+
|
157 |
+
with open(filepath, encoding="utf-8") as f:
|
158 |
+
id_ = -1
|
159 |
+
for line in f:
|
160 |
+
if line == "" or line == "\n":
|
161 |
+
continue
|
162 |
+
else:
|
163 |
+
splits = line.split("\t")
|
164 |
+
id_ += 1
|
165 |
+
yield id_, {
|
166 |
+
"id": str(id_),
|
167 |
+
"domain": splits[0],
|
168 |
+
"tokens": splits[2].split(" "),
|
169 |
+
"ner_tags": splits[1].split(" "),
|
170 |
+
}
|