|
--- |
|
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](#table-of-contents) |
|
- [Dataset Description](#dataset-description) |
|
- [Dataset Summary](#dataset-summary) |
|
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
|
- [Languages](#languages) |
|
- [Dataset Structure](#dataset-structure) |
|
- [Data Instances](#data-instances) |
|
- [Data Fields](#data-fields) |
|
- [Data Splits](#data-splits) |
|
- [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) |
|
- [Contributions](#contributions) |
|
|
|
## Dataset Description |
|
|
|
- **Homepage:** [CoNLL-2012 Shared Task](https://conll.cemantix.org/2012/data.html), [Author's page](https://cemantix.org/data/ontonotes.html) |
|
- **Repository:** [Mendeley](https://data.mendeley.com/datasets/zmycy7t9h9) |
|
- **Paper:** [Towards Robust Linguistic Analysis using OntoNotes](https://aclanthology.org/W13-3516/) |
|
- **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](https://data.mendeley.com/datasets/zmycy7t9h9), 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](https://paperswithcode.com/dataset/ontonotes-5-0) and [CoNLL-2012](https://paperswithcode.com/dataset/conll-2012-1) |
|
|
|
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)](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ontonotes-v5) |
|
- [Coreference Resolution on OntoNotes](https://paperswithcode.com/sota/coreference-resolution-on-ontonotes) |
|
- [Semantic Role Labeling on OntoNotes](https://paperswithcode.com/sota/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': [], |
|
} |
|
``` |
|
|
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### Data Fields |
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- **`document_id`** (*`str`*): This is a variation on the document filename |
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- **`sentences`** (*`List[Dict]`*): All sentences of the same document are in a single example for the convenience of concatenating sentences. |
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Every element in `sentences` is a *`Dict`* composed of the following data fields: |
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- **`part_id`** (*`int`*) : Some files are divided into multiple parts numbered as 000, 001, 002, ... etc. |
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- **`words`** (*`List[str]`*) : |
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- **`pos_tags`** (*`List[ClassLabel]` or `List[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. |
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- 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](https://data.mendeley.com/datasets/zmycy7t9h9) |
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- arabic : str. Because pos tag in Arabic is compounded and complex, hard to represent it by `ClassLabel` |
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- 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",])`, where `X` is for pos tag missing |
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- 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",])`, where `XX` is for pos tag missing, and `-LRB-`/`-RRB-` is "`(`" / "`)`". |
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- 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",])`, where `XX` is for pos tag missing, and `-LRB-`/`-RRB-` is "`(`" / "`)`". |
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- **`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 be `None`. |
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- **`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 are `None`. |
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- **`predicate_framenet_ids`** (*`List[Optional[int]]`*) : The PropBank frameset ID of the lemmas in predicate_lemmas, or `None`. |
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- **`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. |
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- **`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 be `None`. |
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- **`named_entities`** (*`List[ClassLabel]`*) : The BIO tags for named entities in the sentence. |
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- 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",])` |
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- **`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. |
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- **`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. |
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### Data Splits |
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Each dataset (arabic_v4, chinese_v4, english_v4, english_v12) has 3 splits: _train_, _validation_, and _test_ |
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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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[More Information Needed] |
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### Licensing Information |
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[More Information Needed] |
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### Citation Information |
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``` |
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@inproceedings{pradhan-etal-2013-towards, |
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title = "Towards Robust Linguistic Analysis using {O}nto{N}otes", |
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author = {Pradhan, Sameer and |
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Moschitti, Alessandro and |
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Xue, Nianwen and |
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Ng, Hwee Tou and |
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Bj{\"o}rkelund, Anders and |
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Uryupina, Olga and |
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Zhang, Yuchen and |
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Zhong, Zhi}, |
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booktitle = "Proceedings of the Seventeenth Conference on Computational Natural Language Learning", |
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month = aug, |
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year = "2013", |
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address = "Sofia, Bulgaria", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/W13-3516", |
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pages = "143--152", |
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} |
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``` |
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### Contributions |
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Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset. |
