The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups.

The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on a separate line and there is an empty line after each sentence. The first item on each line is a word, the second a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2 tagging scheme, whereas the original dataset uses IOB1.

For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419

The shared task of CoNLL-2003 concerns language-independent named entity recognition and concentrates on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('conll2003', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups.

The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on a separate line and there is an empty line after each sentence. The first item on each line is a word, the second a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2 tagging scheme, whereas the original dataset uses IOB1.

For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419

The CoNLL dataset is a widely used resource in the field of natural language processing (NLP). The term “CoNLL” stands for Conference on Natural Language Learning. It originates from a series of shared tasks organized at the Conferences of Natural Language Learning.

Dataset

This dataset was created by GONG ZEQUN

Contents

This is a trained model for the supervised machine learning tool NameTag 3 (https://ufal.mff.cuni.cz/nametag/3/), trained jointly on several NE corpora: English CoNLL-2003, German CoNLL-2003, Dutch CoNLL-2002, Spanish CoNLL-2002, Ukrainian Lang-uk, and Czech CNEC 2.0, all harmonized to flat NEs with 4 labels PER, ORG, LOC, and MISC. NameTag 3 is an open-source tool for both flat and nested named entity recognition (NER). NameTag 3 identifies proper names in text and classifies them into a set of predefined categories, such as names of persons, locations, organizations, etc. The model documentation can be found at https://ufal.mff.cuni.cz/nametag/3/models#multilingual-conll.

This is a trained model for the supervised machine learning tool NameTag 3 (https://ufal.mff.cuni.cz/nametag/3/), trained jointly on several NE corpora: English CoNLL-2003, German CoNLL-2003, Dutch CoNLL-2002, Spanish CoNLL-2002, Ukrainian Lang-uk, and Czech CNEC 2.0, all harmonized to flat NEs with 4 labels PER, ORG, LOC, and MISC. NameTag 3 is an open-source tool for both flat and nested named entity recognition (NER). NameTag 3 identifies proper names in text and classifies them into a set of predefined categories, such as names of persons, locations, organizations, etc. The model documentation can be found at https://ufal.mff.cuni.cz/nametag/3/models#multilingual-conll.

Ritu2/conll2003 dataset hosted on Hugging Face and contributed by the HF Datasets community

A dataset of financial agreements made public through U.S. Security and Exchange Commission (SEC) filings. Eight documents (totalling 54,256 words) were randomly selected for manual annotation, based on the four NE types provided in the CoNLL-2003 dataset: LOCATION (LOC), ORGANISATION (ORG), PERSON (PER), and MISCELLANEOUS (MISC).

This is the data associated with Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy and James R. Curran (2013), "Learning multilingual named entity recognition from Wikipedia", Artificial Intelligence 194 (DOI: 10.1016/j.artint.2012.03.006). A preprint is included here as wikiner-preprint.pdfThis data was originally available at http://schwa.org/resources (which linked to http://schwa.org/projects/resources/wiki/Wikiner).The .bz2 files are NER training corpora produced as reported in the Artificial Intelligence paper. wp2 and wp3 are differentiated by wp3 using a higher level of link inference. They use a pipe-delimited format that can be converted to CoNLL 2003 format with system2conll.pl.nothman08types.tsv is a manual classification of articles first used in Joel Nothman, James R. Curran and Tara Murphy (2008), "Transforming Wikipedia into Named Entity Training Data", In Proceedings of the Australasian Language Technology Association Workshop 2008. http://aclanthology.coli.uni-saarland.de/pdf/U/U08/U08-1016.pdfpopular.tsv and random.tsv are manual article classifications developed for the Artifiical Intelligence paper based on different strategies for sampling articles from Wikipedia in order to account for Wikipedia's biased distribution (see that paper). scheme.tsv maps these fine-grained labels to coarser annotations including CoNLL 2003-style.wikigold.conll.txt is a manual NER annotation of some Wikipedia text as presented in Dominic Balasuriya and Nicky Ringland and Joel Nothman and Tara Murphy and James R. Curran (2009), in Proceedings of the 2009 Workshop on The People's Web Meets NLP: Collaboratively Constructed Semantic Resources (http://www.aclweb.org/anthology/W/W09/W09-3302).See also corpora produced similarly in an enhanced version of this work work (Pan et al., "Cross-lingual Name Tagging and Linking for 282 Languages", ACL 2017) at http://nlp.cs.rpi.edu/wikiann/.

Dataset

This dataset was created by Julian Garratt

Contents

Dataset

This dataset was created by vbichphuong

Contents

Dataset Card for AutoTrain Evaluator

This repository contains model predictions generated by AutoTrain for the following task and dataset:

Task: Token Classification Model: sarahmiller137/distilbert-base-uncased-ft-conll2003 Dataset: conll2003 Config: conll2003 Split: test

To run new evaluation jobs, visit Hugging Face's automatic model evaluator.

  Contributions

Thanks to @sarahmiller137 for evaluating this model.

Danish Dependency Treebank (DaNE) is a named entity annotation for the Danish Universal Dependencies treebank using the CoNLL-2003 annotation scheme.

Dataset

This dataset was created by shweta sharma

Contents

Dataset contains 50 Supreme Court of India Court Decisions annotated for Named Entity Recognition in the case documents with three different encoding schemes viz., IOB, IOBES, BILOU. The dataset is created using the CoNLL-2003 format.

ParlaMint 3.0 is a multilingual set of 26 comparable corpora containing parliamentary debates mostly starting in 2015 and extending to mid-2022, with the individual corpora being between 9 and 125 million words in size. The corpora have extensive metadata, including aspects of the parliament; the speakers (name, gender, MP status, party affiliation, party coalition/opposition); are structured into time-stamped terms, sessions and meetings; and with speeches being marked by the speaker and their role (e.g. chair, regular speaker). The speeches also contain marked-up transcriber comments, such as gaps in the transcription, interruptions, applause, etc. Note that some corpora have further information, e.g. the year of birth of the speakers, links to their Wikipedia articles, their membership in various committees, etc. The corpora are also marked to the subcorpus they belong to ("reference", until 2020-01-30, "covid", from 2020-01-31, and "war", from 2022-02-24). This entry contains the linguistically marked-up version of the corpora, while the text version is available at http://hdl.handle.net/11356/1486. The ParlaMint.ana linguistic annotation includes tokenization, sentence segmentation, lemmatisation, Universal Dependencies part-of-speech, morphological features, and syntactic dependencies, and the 4-class CoNLL-2003 named entities. Some corpora also have further linguistic annotations, such as PoS tagging or named entities according to language-specific schemes, with their corpus TEI headers giving further details on the annotation vocabularies and tools. The compressed files include the ParlaMint.ana XML TEI-encoded linguistically annotated corpora; the derived corpora in CoNLL-U with TSV speech metadata; and the vertical files (with registry file), suitable for use with CQP-based concordancers, such as CWB, noSketch Engine or KonText. Also included is the 3.0 release of the data and scripts available at the GitHub repository of the ParlaMint project. As opposed to the previous version 2.1, this version corrects some errors in various corpora and adds the information on upper / lower house for bicameral parliaments. The vertical files have also been changed to make them easier to use in the concordancers.

autoevaluate/autoeval-eval-conll2003-conll2003-623e8b-1865063749 dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for AutoTrain Evaluator

This repository contains model predictions generated by AutoTrain for the following task and dataset:

Task: Token Classification Model: AIventurer/bert-finetuned-ner Dataset: conll2003 Config: conll2003 Split: test

To run new evaluation jobs, visit Hugging Face's automatic model evaluator.

  Contributions

Thanks to @Anmol-Hexaware for evaluating this model.

autoevaluate/autoeval-staging-eval-project-conll2003-8cabc0e2-10785450 dataset hosted on Hugging Face and contributed by the HF Datasets community