데이터셋 탐색

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.

CoNLL-2003 is a named entity recognition dataset released as a part of CoNLL-2003 shared task: language-independent named entity recognition. The data consists of eight files covering two languages: English and German. For each of the languages there is a training file, a development file, a test file and a large file with unannotated data. The English data was taken from the Reuters Corpus. This corpus consists of Reuters news stories between August 1996 and August 1997. For the training and development set, ten days worth of data were taken from the files representing the end of August 1996. For the test set, the texts were from December 1996. The preprocessed raw data covers the month of September 1996. The text for the German data was taken from the ECI Multilingual Text Corpus. This corpus consists of texts in many languages. The portion of data that was used for this task, was extracted from the German newspaper Frankfurter Rundshau. All three of the training, development and test sets were taken from articles written in one week at the end of August 1992. The raw data were taken from the months of September to December 1992. | English data | Articles | Sentences | Tokens | LOC | MISC | ORG | PER | |-------------------|----------|-----------|---------|------|------|------|------| | Training set | 946 | 14,987 | 203,621 | 7140 | 3438 | 6321 | 6600 | | Development set | 216 | 3,466 | 51,362 | 1837 | 922 | 1341 | 1842 | | Test set | 231 | 3,684 | 46,435 | 1668 | 702 | 1661 | 1617 | Number of articles, sentences, tokens and entities (locations, miscellaneous, organizations, and persons) in English data files. | German data | Articles | Sentences | Tokens | LOC | MISC | ORG | PER | |-------------------|----------|-----------|---------|------|------|------|------| | Training set | 553 | 12,705 | 206,931 | 4363 | 2288 | 2427 | 2773 | | Development set | 201 | 3,068 | 51,444 | 1181 | 1010 | 1241 | 1401 | | Test set | 155 | 3,160 | 51,943 | 1035 | 670 | 773 | 1195 | Number of articles, sentences, tokens and entities (locations, miscellaneous, organizations, and persons) in German data files.

The CoNLL-2012 shared task involved predicting coreference in English, Chinese, and Arabic, using the final version, v5.0, of the OntoNotes corpus. It was a follow-on to the English-only task organized in 2011. Source: Pradhan et al.

CoNLL-2014 will continue the CoNLL tradition of having a high profile shared task in natural language processing. This year's shared task will be grammatical error correction, a continuation of the CoNLL shared task in 2013. A participating system in this shared task is given short English texts written by non-native speakers of English. The system detects the grammatical errors present in the input texts, and returns the corrected essays. The shared task in 2014 will require a participating system to correct all errors present in an essay (i.e., not restricted to just five error types in 2013). Also, the evaluation metric will be changed to F0.5, weighting precision twice as much as recall. The grammatical error correction task is impactful since it is estimated that hundreds of millions of people in the world are learning English and they benefit directly from an automated grammar checker. However, for many error types, current grammatical error correction methods do not achieve a high performance and thus more research is needed. Source: CoNLL-2014 Shared Task: Grammatical Error Correction Image source: Tou Ng et al.

The shared task of CoNLL-2002 concerns language-independent named entity recognition. The types of named entities include: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The participants of the shared task were offered training and test data for at least two languages. Information sources other than the training data might have been used in this shared task. Source: CoNLL 2002 Image Source: https://www.aclweb.org/anthology/W02-2024.pdf

AIDA CoNLL-YAGO contains assignments of entities to the mentions of named entities annotated for the original CoNLL 2003 entity recognition task. The entities are identified by YAGO2 entity name, by Wikipedia URL, or by Freebase mid. Source: AIDA CoNLL-YAGO

CoNLL++ is a corrected version of the CoNLL03 NER dataset where 5.38% of the test sentences have been fixed. Source: CrossWeigh: Training Named Entity Tagger from Imperfect Annotations

The CoNLL04 dataset is a benchmark dataset used for relation extraction tasks. It contains 1,437 sentences, each of which has at least one relation. The sentences are annotated with information about entities and their corresponding relation types.

CoNLL-2000 is a dataset for dividing text into syntactically related non-overlapping groups of words, so-called text chunking. Source: https://www.clips.uantwerpen.be/conll2000/chunking/

The task builds on the CoNLL-2008 task and extends it to multiple languages. The core of the task is to predict syntactic and semantic dependencies and their labeling. Data is provided for both statistical training and evaluation, which extract these labeled dependencies from manually annotated treebanks such as the Penn Treebank for English, the Prague Dependency Treebank for Czech and similar treebanks for Catalan, Chinese, German, Japanese and Spanish languages, enriched with semantic relations (such as those captured in the Prop/Nombank and similar resources). Great effort has been devoted to provide the participants with a common and relatively simple data representation for all the languages, similar to the last year's English data.

A test dataset that annotated articles in 2020 following the CoNLL-2003 NER task.

Automatic segmentation, tokenization and morphological and syntactic annotations of raw texts in 45 languages, generated by UDPipe (http://ufal.mff.cuni.cz/udpipe), together with word embeddings of dimension 100 computed from lowercased texts by word2vec (https://code.google.com/archive/p/word2vec/).