Datasets:
metadata
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ar
- en
- zh
license:
- cc-by-nc-nd-4.0
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
- part-of-speech
- coreference-resolution
- parsing
- lemmatization
- word-sense-disambiguation
paperswithcode_id: ontonotes-5-0
pretty_name: CoNLL2012 shared task data based on OntoNotes 5.0
tags:
- semantic-role-labeling
dataset_info:
- config_name: english_v4
features:
- name: document_id
dtype: string
- name: sentences
list:
- name: part_id
dtype: int32
- name: words
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': XX
'1': '``'
'2': $
'3': ''''''
'4': ','
'5': '-LRB-'
'6': '-RRB-'
'7': .
'8': ':'
'9': ADD
'10': AFX
'11': CC
'12': CD
'13': DT
'14': EX
'15': FW
'16': HYPH
'17': IN
'18': JJ
'19': JJR
'20': JJS
'21': LS
'22': MD
'23': NFP
'24': NN
'25': NNP
'26': NNPS
'27': NNS
'28': PDT
'29': POS
'30': PRP
'31': PRP$
'32': RB
'33': RBR
'34': RBS
'35': RP
'36': SYM
'37': TO
'38': UH
'39': VB
'40': VBD
'41': VBG
'42': VBN
'43': VBP
'44': VBZ
'45': WDT
'46': WP
'47': WP$
'48': WRB
- name: parse_tree
dtype: string
- name: predicate_lemmas
sequence: string
- name: predicate_framenet_ids
sequence: string
- name: word_senses
sequence: float32
- name: speaker
dtype: string
- name: named_entities
sequence:
class_label:
names:
'0': O
'1': B-PERSON
'2': I-PERSON
'3': B-NORP
'4': I-NORP
'5': B-FAC
'6': I-FAC
'7': B-ORG
'8': I-ORG
'9': B-GPE
'10': I-GPE
'11': B-LOC
'12': I-LOC
'13': B-PRODUCT
'14': I-PRODUCT
'15': B-DATE
'16': I-DATE
'17': B-TIME
'18': I-TIME
'19': B-PERCENT
'20': I-PERCENT
'21': B-MONEY
'22': I-MONEY
'23': B-QUANTITY
'24': I-QUANTITY
'25': B-ORDINAL
'26': I-ORDINAL
'27': B-CARDINAL
'28': I-CARDINAL
'29': B-EVENT
'30': I-EVENT
'31': B-WORK_OF_ART
'32': I-WORK_OF_ART
'33': B-LAW
'34': I-LAW
'35': B-LANGUAGE
'36': I-LANGUAGE
- name: srl_frames
list:
- name: verb
dtype: string
- name: frames
sequence: string
- name: coref_spans
sequence:
sequence: int32
length: 3
splits:
- name: train
num_bytes: 112246121
num_examples: 1940
- name: validation
num_bytes: 14116925
num_examples: 222
- name: test
num_bytes: 14709044
num_examples: 222
download_size: 193644139
dataset_size: 141072090
- config_name: chinese_v4
features:
- name: document_id
dtype: string
- name: sentences
list:
- name: part_id
dtype: int32
- name: words
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': X
'1': AD
'2': AS
'3': BA
'4': CC
'5': CD
'6': CS
'7': DEC
'8': DEG
'9': DER
'10': DEV
'11': DT
'12': ETC
'13': FW
'14': IJ
'15': INF
'16': JJ
'17': LB
'18': LC
'19': M
'20': MSP
'21': NN
'22': NR
'23': NT
'24': OD
'25': 'ON'
'26': P
'27': PN
'28': PU
'29': SB
'30': SP
'31': URL
'32': VA
'33': VC
'34': VE
'35': VV
- name: parse_tree
dtype: string
- name: predicate_lemmas
sequence: string
- name: predicate_framenet_ids
sequence: string
- name: word_senses
sequence: float32
- name: speaker
dtype: string
- name: named_entities
sequence:
class_label:
names:
'0': O
'1': B-PERSON
'2': I-PERSON
'3': B-NORP
'4': I-NORP
'5': B-FAC
'6': I-FAC
'7': B-ORG
'8': I-ORG
'9': B-GPE
'10': I-GPE
'11': B-LOC
'12': I-LOC
'13': B-PRODUCT
'14': I-PRODUCT
'15': B-DATE
'16': I-DATE
'17': B-TIME
'18': I-TIME
'19': B-PERCENT
'20': I-PERCENT
'21': B-MONEY
'22': I-MONEY
'23': B-QUANTITY
'24': I-QUANTITY
'25': B-ORDINAL
'26': I-ORDINAL
'27': B-CARDINAL
'28': I-CARDINAL
'29': B-EVENT
'30': I-EVENT
'31': B-WORK_OF_ART
'32': I-WORK_OF_ART
'33': B-LAW
'34': I-LAW
'35': B-LANGUAGE
'36': I-LANGUAGE
- name: srl_frames
list:
- name: verb
dtype: string
- name: frames
sequence: string
- name: coref_spans
sequence:
sequence: int32
length: 3
splits:
- name: train
num_bytes: 77195698
num_examples: 1391
- name: validation
num_bytes: 10828169
num_examples: 172
- name: test
num_bytes: 9585138
num_examples: 166
download_size: 193644139
dataset_size: 97609005
- config_name: arabic_v4
features:
- name: document_id
dtype: string
- name: sentences
list:
- name: part_id
dtype: int32
- name: words
sequence: string
- name: pos_tags
sequence: string
- name: parse_tree
dtype: string
- name: predicate_lemmas
sequence: string
- name: predicate_framenet_ids
sequence: string
- name: word_senses
sequence: float32
- name: speaker
dtype: string
- name: named_entities
sequence:
class_label:
names:
'0': O
'1': B-PERSON
'2': I-PERSON
'3': B-NORP
'4': I-NORP
'5': B-FAC
'6': I-FAC
'7': B-ORG
'8': I-ORG
'9': B-GPE
'10': I-GPE
'11': B-LOC
'12': I-LOC
'13': B-PRODUCT
'14': I-PRODUCT
'15': B-DATE
'16': I-DATE
'17': B-TIME
'18': I-TIME
'19': B-PERCENT
'20': I-PERCENT
'21': B-MONEY
'22': I-MONEY
'23': B-QUANTITY
'24': I-QUANTITY
'25': B-ORDINAL
'26': I-ORDINAL
'27': B-CARDINAL
'28': I-CARDINAL
'29': B-EVENT
'30': I-EVENT
'31': B-WORK_OF_ART
'32': I-WORK_OF_ART
'33': B-LAW
'34': I-LAW
'35': B-LANGUAGE
'36': I-LANGUAGE
- name: srl_frames
list:
- name: verb
dtype: string
- name: frames
sequence: string
- name: coref_spans
sequence:
sequence: int32
length: 3
splits:
- name: train
num_bytes: 42017761
num_examples: 359
- name: validation
num_bytes: 4859292
num_examples: 44
- name: test
num_bytes: 4900664
num_examples: 44
download_size: 193644139
dataset_size: 51777717
- config_name: english_v12
features:
- name: document_id
dtype: string
- name: sentences
list:
- name: part_id
dtype: int32
- name: words
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': XX
'1': '``'
'2': $
'3': ''''''
'4': '*'
'5': ','
'6': '-LRB-'
'7': '-RRB-'
'8': .
'9': ':'
'10': ADD
'11': AFX
'12': CC
'13': CD
'14': DT
'15': EX
'16': FW
'17': HYPH
'18': IN
'19': JJ
'20': JJR
'21': JJS
'22': LS
'23': MD
'24': NFP
'25': NN
'26': NNP
'27': NNPS
'28': NNS
'29': PDT
'30': POS
'31': PRP
'32': PRP$
'33': RB
'34': RBR
'35': RBS
'36': RP
'37': SYM
'38': TO
'39': UH
'40': VB
'41': VBD
'42': VBG
'43': VBN
'44': VBP
'45': VBZ
'46': VERB
'47': WDT
'48': WP
'49': WP$
'50': WRB
- name: parse_tree
dtype: string
- name: predicate_lemmas
sequence: string
- name: predicate_framenet_ids
sequence: string
- name: word_senses
sequence: float32
- name: speaker
dtype: string
- name: named_entities
sequence:
class_label:
names:
'0': O
'1': B-PERSON
'2': I-PERSON
'3': B-NORP
'4': I-NORP
'5': B-FAC
'6': I-FAC
'7': B-ORG
'8': I-ORG
'9': B-GPE
'10': I-GPE
'11': B-LOC
'12': I-LOC
'13': B-PRODUCT
'14': I-PRODUCT
'15': B-DATE
'16': I-DATE
'17': B-TIME
'18': I-TIME
'19': B-PERCENT
'20': I-PERCENT
'21': B-MONEY
'22': I-MONEY
'23': B-QUANTITY
'24': I-QUANTITY
'25': B-ORDINAL
'26': I-ORDINAL
'27': B-CARDINAL
'28': I-CARDINAL
'29': B-EVENT
'30': I-EVENT
'31': B-WORK_OF_ART
'32': I-WORK_OF_ART
'33': B-LAW
'34': I-LAW
'35': B-LANGUAGE
'36': I-LANGUAGE
- name: srl_frames
list:
- name: verb
dtype: string
- name: frames
sequence: string
- name: coref_spans
sequence:
sequence: int32
length: 3
splits:
- name: train
num_bytes: 174173192
num_examples: 10539
- name: validation
num_bytes: 24264804
num_examples: 1370
- name: test
num_bytes: 18254144
num_examples: 1200
download_size: 193644139
dataset_size: 216692140
Dataset Card for CoNLL2012 shared task data based on OntoNotes 5.0
Table of Contents
- Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: CoNLL-2012 Shared Task, Author's page
- Repository: Mendeley
- Paper: Towards Robust Linguistic Analysis using OntoNotes
- Leaderboard:
- Point of Contact:
Dataset Summary
OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre, multilingual corpus manually annotated with syntactic, semantic and discourse information.
This dataset is the version of OntoNotes v5.0 extended and is used in the CoNLL-2012 shared task. It includes v4 train/dev and v9 test data for English/Chinese/Arabic and corrected version v12 train/dev/test data (English only).
The source of data is the Mendeley Data repo ontonotes-conll2012, which seems to be as the same as the official data, but users should use this dataset on their own responsibility.
See also summaries from paperwithcode, OntoNotes 5.0 and CoNLL-2012
For more detailed info of the dataset like annotation, tag set, etc., you can refer to the documents in the Mendeley repo mentioned above.
Supported Tasks and Leaderboards
- Named Entity Recognition on Ontonotes v5 (English)
- Coreference Resolution on OntoNotes
- Semantic Role Labeling on OntoNotes
- ...
Languages
V4 data for Arabic, Chinese, English, and V12 data for English
Dataset Structure
Data Instances
{
{'document_id': 'nw/wsj/23/wsj_2311',
'sentences': [{'part_id': 0,
'words': ['CONCORDE', 'trans-Atlantic', 'flights', 'are', '$', '2, 'to', 'Paris', 'and', '$', '3, 'to', 'London', '.']},
'pos_tags': [25, 18, 27, 43, 2, 12, 17, 25, 11, 2, 12, 17, 25, 7],
'parse_tree': '(TOP(S(NP (NNP CONCORDE) (JJ trans-Atlantic) (NNS flights) )(VP (VBP are) (NP(NP(NP ($ $) (CD 2,400) )(PP (IN to) (NP (NNP Paris) ))) (CC and) (NP(NP ($ $) (CD 3,200) )(PP (IN to) (NP (NNP London) ))))) (. .) ))',
'predicate_lemmas': [None, None, None, 'be', None, None, None, None, None, None, None, None, None, None],
'predicate_framenet_ids': [None, None, None, '01', None, None, None, None, None, None, None, None, None, None],
'word_senses': [None, None, None, None, None, None, None, None, None, None, None, None, None, None],
'speaker': None,
'named_entities': [7, 6, 0, 0, 0, 15, 0, 5, 0, 0, 15, 0, 5, 0],
'srl_frames': [{'frames': ['B-ARG1', 'I-ARG1', 'I-ARG1', 'B-V', 'B-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'I-ARG2', 'O'],
'verb': 'are'}],
'coref_spans': [],
{'part_id': 0,
'words': ['In', 'a', 'Centennial', 'Journal', 'article', 'Oct.', '5', ',', 'the', 'fares', 'were', 'reversed', '.']}]}
'pos_tags': [17, 13, 25, 25, 24, 25, 12, 4, 13, 27, 40, 42, 7],
'parse_tree': '(TOP(S(PP (IN In) (NP (DT a) (NML (NNP Centennial) (NNP Journal) ) (NN article) ))(NP (NNP Oct.) (CD 5) ) (, ,) (NP (DT the) (NNS fares) )(VP (VBD were) (VP (VBN reversed) )) (. .) ))',
'predicate_lemmas': [None, None, None, None, None, None, None, None, None, None, None, 'reverse', None],
'predicate_framenet_ids': [None, None, None, None, None, None, None, None, None, None, None, '01', None],
'word_senses': [None, None, None, None, None, None, None, None, None, None, None, None, None],
'speaker': None,
'named_entities': [0, 0, 4, 22, 0, 12, 30, 0, 0, 0, 0, 0, 0],
'srl_frames': [{'frames': ['B-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'I-ARGM-LOC', 'B-ARGM-TMP', 'I-ARGM-TMP', 'O', 'B-ARG1', 'I-ARG1', 'O', 'B-V', 'O'],
'verb': 'reversed'}],
'coref_spans': [],
}
Data Fields
document_id(str): This is a variation on the document filenamesentences(List[Dict]): All sentences of the same document are in a single example for the convenience of concatenating sentences.
Every element in sentences is a Dict composed of the following data fields:
part_id(int) : Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.words(List[str]) :pos_tags(List[ClassLabel]orList[str]) : This is the Penn-Treebank-style part of speech. When parse information is missing, all parts of speech except the one for which there is some sense or proposition annotation are marked with a XX tag. The verb is marked with just a VERB tag.- tag set : Note tag sets below are founded by scanning all the data, and I found it seems to be a little bit different from officially stated tag sets. See official documents in the Mendeley repo
- arabic : str. Because pos tag in Arabic is compounded and complex, hard to represent it by
ClassLabel - chinese v4 :
datasets.ClassLabel(num_classes=36, names=["X", "AD", "AS", "BA", "CC", "CD", "CS", "DEC", "DEG", "DER", "DEV", "DT", "ETC", "FW", "IJ", "INF", "JJ", "LB", "LC", "M", "MSP", "NN", "NR", "NT", "OD", "ON", "P", "PN", "PU", "SB", "SP", "URL", "VA", "VC", "VE", "VV",]), whereXis for pos tag missing - english v4 :
datasets.ClassLabel(num_classes=49, names=["XX", "``", "$", "''", ",", "-LRB-", "-RRB-", ".", ":", "ADD", "AFX", "CC", "CD", "DT", "EX", "FW", "HYPH", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NFP", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "WDT", "WP", "WP$", "WRB",]), whereXXis for pos tag missing, and-LRB-/-RRB-is "(" / ")". - english v12 :
datasets.ClassLabel(num_classes=51, names="english_v12": ["XX", "``", "$", "''", "*", ",", "-LRB-", "-RRB-", ".", ":", "ADD", "AFX", "CC", "CD", "DT", "EX", "FW", "HYPH", "IN", "JJ", "JJR", "JJS", "LS", "MD", "NFP", "NN", "NNP", "NNPS", "NNS", "PDT", "POS", "PRP", "PRP$", "RB", "RBR", "RBS", "RP", "SYM", "TO", "UH", "VB", "VBD", "VBG", "VBN", "VBP", "VBZ", "VERB", "WDT", "WP", "WP$", "WRB",]), whereXXis for pos tag missing, and-LRB-/-RRB-is "(" / ")".
- arabic : str. Because pos tag in Arabic is compounded and complex, hard to represent it by
- tag set : Note tag sets below are founded by scanning all the data, and I found it seems to be a little bit different from officially stated tag sets. See official documents in the Mendeley repo
parse_tree(Optional[str]) : An serialized NLTK Tree representing the parse. It includes POS tags as pre-terminal nodes. When the parse information is missing, the parse will beNone.predicate_lemmas(List[Optional[str]]) : The predicate lemma of the words for which we have semantic role information or word sense information. All other indices areNone.predicate_framenet_ids(List[Optional[int]]) : The PropBank frameset ID of the lemmas in predicate_lemmas, orNone.word_senses(List[Optional[float]]) : The word senses for the words in the sentence, or None. These are floats because the word sense can have values after the decimal, like 1.1.speaker(Optional[str]) : This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data. When it is not available, it will beNone.named_entities(List[ClassLabel]) : The BIO tags for named entities in the sentence.- tag set :
datasets.ClassLabel(num_classes=37, names=["O", "B-PERSON", "I-PERSON", "B-NORP", "I-NORP", "B-FAC", "I-FAC", "B-ORG", "I-ORG", "B-GPE", "I-GPE", "B-LOC", "I-LOC", "B-PRODUCT", "I-PRODUCT", "B-DATE", "I-DATE", "B-TIME", "I-TIME", "B-PERCENT", "I-PERCENT", "B-MONEY", "I-MONEY", "B-QUANTITY", "I-QUANTITY", "B-ORDINAL", "I-ORDINAL", "B-CARDINAL", "I-CARDINAL", "B-EVENT", "I-EVENT", "B-WORK_OF_ART", "I-WORK_OF_ART", "B-LAW", "I-LAW", "B-LANGUAGE", "I-LANGUAGE",])
- tag set :
srl_frames(List[{"word":str, "frames":List[str]}]) : A dictionary keyed by the verb in the sentence for the given Propbank frame labels, in a BIO format.coref spans(List[List[int]]) : The spans for entity mentions involved in coreference resolution within the sentence. Each element is a tuple composed of (cluster_id, start_index, end_index). Indices are inclusive.
Data Splits
Each dataset (arabic_v4, chinese_v4, english_v4, english_v12) has 3 splits: train, validation, and test
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
@inproceedings{pradhan-etal-2013-towards,
title = "Towards Robust Linguistic Analysis using {O}nto{N}otes",
author = {Pradhan, Sameer and
Moschitti, Alessandro and
Xue, Nianwen and
Ng, Hwee Tou and
Bj{\"o}rkelund, Anders and
Uryupina, Olga and
Zhang, Yuchen and
Zhong, Zhi},
booktitle = "Proceedings of the Seventeenth Conference on Computational Natural Language Learning",
month = aug,
year = "2013",
address = "Sofia, Bulgaria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W13-3516",
pages = "143--152",
}
Contributions
Thanks to @richarddwang for adding this dataset.