HiTZ/GoLLIE-7B
Text Generation
•
Updated
•
5.41k
•
15
Datasets:
Tasks:
Token Classification
Multilinguality:
multilingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
License:
document_id
stringlengths 18
18
| sentences
list |
|---|---|
nw/ann/00/ann_0001 | [
{
"part_id": 0,
"words": [
"650#DEFAULT#650#650",
"جُنْدِيّاً#junodiy~#jndyAF#junodiy~+AF",
"أَمِيرْكِيّاً#>amoriykiy~#>myrkyAF#>amiyrokiy~+AF",
"إِلَى#<ilaY#<lY#<ilaY",
"الفِيلِيبِّين#fiyliyb~iyn#Alfylybyn#Al+fiyliyb~iyn",
"مِن#min#mn#min",
"اليَوْمِ#yawom#Alywm#Al+yawom+i",
"فِي#fiy#fy#fiy",
"بِعْثَةٍ#baEovap#bEvp#biEov+ap+K",
"تَدْرِيبِيَّةٍ#tadoriybiy~#tdrybyp#tadoriybiy~+ap+K",
"لِ-#clitics#l#li-",
"-القَضاءِ#qaDA'#AlqDA'#-Al+qaDA'+i",
"عَلَى#EalaY#ElY#EalaY",
"\"#DEFAULT#\"#\"",
"أَبُو#>abuw#>bw#>abuw",
"سَيّاف#say~Af#syAf#say~Af",
"\"#DEFAULT#\"#\""
],
"pos_tags": [
"NOUN_NUM",
"NOUN+CASE_INDEF_ACC",
"ADJ+CASE_INDEF_ACC",
"PREP",
"DET+NOUN_PROP",
"PREP",
"DET+NOUN+CASE_DEF_GEN",
"PREP",
"NOUN+NSUFF_FEM_SG+CASE_INDEF_GEN",
"ADJ+NSUFF_FEM_SG+CASE_INDEF_GEN",
"PREP",
"DET+NOUN+CASE_DEF_GEN",
"PREP",
"PUNC",
"NOUN_PROP",
"NOUN_PROP",
"PUNC"
],
"parse_tree": "(TOP(FRAG(NP (NOUN_NUM 650#DEFAULT#650#650) (NP (NOUN+CASE_INDEF_ACC جُنْدِيّاً#junodiy~#jndyAF#junodiy~+AF) (ADJ+CASE_INDEF_ACC أَمِيرْكِيّاً#>amoriykiy~#>myrkyAF#>amiyrokiy~+AF) ))(PP (PREP إِلَى#<ilaY#<lY#<ilaY) (NP (DET+NOUN_PROP الفِيلِيبِّين#fiyliyb~iyn#Alfylybyn#Al+fiyliyb~iyn) ))(PP (PREP مِن#min#mn#min) (NP (DET+NOUN+CASE_DEF_GEN اليَوْمِ#yawom#Alywm#Al+yawom+i) ))(PP (PREP فِي#fiy#fy#fiy) (NP(NP (NOUN+NSUFF_FEM_SG+CASE_INDEF_GEN بِعْثَةٍ#baEovap#bEvp#biEov+ap+K) (ADJ+NSUFF_FEM_SG+CASE_INDEF_GEN تَدْرِيبِيَّةٍ#tadoriybiy~#tdrybyp#tadoriybiy~+ap+K) )(PP (PREP لِ-#clitics#l#li-) (NP(NP (DET+NOUN+CASE_DEF_GEN -القَضاءِ#qaDA'#AlqDA'#-Al+qaDA'+i) )(PP (PREP عَلَى#EalaY#ElY#EalaY) (NP (PUNC \"#DEFAULT#\"#\") (NOUN_PROP أَبُو#>abuw#>bw#>abuw) (NOUN_PROP سَيّاف#say~Af#syAf#say~Af) (PUNC \"#DEFAULT#\"#\") ))))))))",
"predicate_lemmas": [
"DEFAULT",
"junodiy~",
">amoriykiy~",
"<ilaY",
"fiyliyb~iyn",
"min",
"yawom",
"fiy",
"baEovap",
"tadoriybiy~",
"clitics",
"qaDA'",
"EalaY",
"DEFAULT",
">abuw",
"say~Af",
"DEFAULT"
],
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"word_senses": [
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"speaker": null,
"named_entities": [
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"srl_frames": [],
"coref_spans": [
[
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[
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{
"part_id": 0,
"words": [
"بَعْدَ-#baEodamA#bEd#baEod+a-",
"-ما#clitics#mA#-mA",
"تَضارَبَت#taDArab#tDArbt#taDArab+at",
"المَعْلُوماتُ#maEoluwm#AlmElwmAt#Al+maEoluwm+At+u",
"الأُسْبُوعَ#>usobuwE#AlAsbwE#Al+>usobuwE+a",
"الماضِيَ#mADiy#AlmADy#Al+mADiy+a",
"عَن#Ean#En#Ean",
"عَدَدِ#Eadad#Edd#Eadad+i",
"الجُنُودِ#junodiy~#Aljnwd#Al+junuwd+i",
"الأَمِيرْكِيِّينَ#>amoriykiy~#AlAmyrkyyn#Al+>amiyrokiy~+iyna",
"الَّذِينَ#Al~a*iy#Al*yn#Al~a*iyna",
"سَ-#clitics#s#sa-",
"-يُشارِكُونَ#$Arak#y$Arkwn#-yu+$Arik+uwna",
"فِي#fiy#fy#fiy",
"بِعْثَةٍ#baEovap#bEvp#biEov+ap+K",
"تَدْرِيبِيَّةٍ#tadoriybiy~#tdrybyp#tadoriybiy~+ap+K",
"لِ-#clitics#l#li-",
"-مُكافَحَةِ#mukAfaHap#mkAfHp#-mukAfaH+ap+i",
"الإِرْهابِ#<irohAb#AlArhAb#Al+<irohAb+i",
"فِي#fiy#fy#fiy",
"الفِيلِيبِّين#fiyliyb~iyn#Alfylybyn#Al+fiyliyb~iyn",
",#DEFAULT#,#,",
"فِي#fiy#fy#fiy",
"إِطارِ#<iTAr#<TAr#<iTAr+i",
"جُهُودِ#jahod#jhwd#juhuwd+i",
"واشِنْطُن#wA$inoTuwn#wA$nTn#wA$inoTun",
"لِ-#clitics#l#li-",
"-مُكافَحَةِ#mukAfaHap#mkAfHp#-mukAfaH+ap+i",
"الإِرْهابِ#<irohAb#AlArhAb#Al+<irohAb+i",
"فِي#fiy#fy#fiy",
"العالَمِ#EAlam#AlEAlm#Al+EAlam+i",
",#DEFAULT#,#,",
"أَفادَ#>afAd#>fAd#>afAd+a",
"أَمْسِ#>amos#>ms#>amos+i",
"مَسْؤُولُونَ#maso&uwl#ms&wlwn#maso&uwl+uwna",
"عَسْكَرِيُّونَ#Easokariy~#Eskrywn#Easokariy~+uwna",
"فِي#fiy#fy#fiy",
"مانِيلا#mAniylA#mAnylA#mAniylA",
"أَنَّ#>an~a#An#>an~a",
"نَحْوَ#naHow#nHw#naHow+a",
"650#DEFAULT#650#650",
"جُنْدِيّاً#junodiy~#jndyAF#junodiy~+AF",
"أَمِيرْكِيّاً#>amoriykiy~#AmyrkyAF#>amiyrokiy~+AF",
"سَ-#clitics#s#sa-",
"-يَنْضَمُّونَ#{inoDam~#ynDm~wn#-ya+noDam~+uwna",
"تِباعاً#tibAE#tbAEAF#tibAE+AF",
"إِلَى#<ilaY#AlY#<ilaY",
"قُوّاتٍ#quw~ap#qwAt#quw~+At+K",
"فِلِيبِّينِيَّةٍ#fiyliyb~iyniy~#fylybynyp#filiyb~iyniy~+ap+K",
"لِ-#clitics#l#li-",
"-تَعْزِيزِ#taEoziyz#tEzyz#-taEoziyz+i",
"قُدْراتِ-#qudorap#qdrAt#qudor+At+i-",
"-ها#clitics#hA#-hA",
"الدِفاعِيَّةِ#difAEiy~#AldfAEyp#Al+difAEiy~+ap+i",
"مِن#min#mn#min",
"أَجْلِ#>ajol#>jl#>ajol+i",
"القَضاءِ#qaDA'#AlqDA'#Al+qaDA'+i",
"عَلَى#EalaY#ElY#EalaY",
"جَماعَةِ#jamAEap#jmAEp#jamAE+ap+i",
"\"#DEFAULT#\"#\"",
"أَبُو#>abuw#>bw#>abuw",
"سَيّاف#say~Af#syAf#say~Af",
"\"#DEFAULT#\"#\"",
"الَّتِي#Al~a*iy#Alty#Al~atiy",
"تَرْبِطُ-#rabaT-ui#trbT#ta+robiT+u-",
"-ها#clitics#hA#-hA",
"صِلاتٌ#Silap#SlAt#Sil+At+N",
"بِ-#clitics#b#bi-",
"-تَنْظِيمِ#tanoZiym#tnZym#-tanoZiym+i",
"\"#DEFAULT#\"#\"",
"القاعِدَةِ#qAEidap#AlqAEdp#Al+qAEid+ap+i",
"\"#DEFAULT#\"#\"",
"الَّذِي#Al~a*iy#Al*y#Al~a*iy",
"يَتَزَعَّمُ-#tazaE~am#ytzEm#ya+tazaE~am+u-",
"-هُ#clitics#h#-hu",
"أُسامَة#>usAmap#AsAmp#>usAm+ap",
"بِن#bin#bn#bin",
"لادِن#lAdin#lAdn#lAdin",
",#DEFAULT#,#,",
"مِن-#mim~A#m#min-",
"-ما#clitics#mA#-mA",
"سَ-#clitics#s#sa-",
"-يُتِيحُ#>atAH#ytyH#-yu+tiyH+u",
"لَ-#li-#l#la-",
"-هُم#clitics#hm#-hum",
"ال{ِنْتِقالَ#{inotiqAl#AlAntqAl#Al+{inotiqAl+a",
"إِلَى#<ilaY#AlY#<ilaY",
"مَناطِقِ#minoTaqap#mnATq#manATiq+i",
"القِتالِ#qitAl#AlqtAl#Al+qitAl+i",
"فِي#fiy#fy#fiy",
"جَنُوبِ#januwb#jnwb#januwb+i",
"البِلادِ#balad#AlblAd#Al+bilAd+i",
".#DEFAULT#.#."
],
"pos_tags": [
"NOUN+CASE_DEF_ACC",
"SUB_CONJ",
"PV+PVSUFF_SUBJ:3FS",
"DET+NOUN+NSUFF_FEM_PL+CASE_DEF_NOM",
"DET+NOUN+CASE_DEF_ACC",
"DET+ADJ+CASE_DEF_ACC",
"PREP",
"NOUN+CASE_DEF_GEN",
"DET+NOUN+CASE_DEF_GEN",
"DET+ADJ+NSUFF_MASC_PL_GEN",
"REL_PRON",
"FUT_PART",
"IV3MP+IV+IVSUFF_SUBJ:MP_MOOD:I",
"PREP",
"NOUN+NSUFF_FEM_SG+CASE_INDEF_GEN",
"ADJ+NSUFF_FEM_SG+CASE_INDEF_GEN",
"PREP",
"NOUN+NSUFF_FEM_SG+CASE_DEF_GEN",
"DET+NOUN+CASE_DEF_GEN",
"PREP",
"DET+NOUN_PROP",
"PUNC",
"PREP",
"NOUN+CASE_DEF_GEN",
"NOUN+CASE_DEF_GEN",
"NOUN_PROP",
"PREP",
"NOUN+NSUFF_FEM_SG+CASE_DEF_GEN",
"DET+NOUN+CASE_DEF_GEN",
"PREP",
"DET+NOUN+CASE_DEF_GEN",
"PUNC",
"PV+PVSUFF_SUBJ:3MS",
"NOUN+CASE_DEF_GEN",
"NOUN+NSUFF_MASC_PL_NOM",
"ADJ+NSUFF_MASC_PL_NOM",
"PREP",
"NOUN_PROP",
"SUB_CONJ",
"NOUN+CASE_DEF_ACC",
"NOUN_NUM",
"NOUN+CASE_INDEF_ACC",
"ADJ+CASE_INDEF_ACC",
"FUT_PART",
"IV3MP+IV+IVSUFF_SUBJ:MP_MOOD:I",
"NOUN+CASE_INDEF_ACC",
"PREP",
"NOUN+NSUFF_FEM_PL+CASE_INDEF_GEN",
"ADJ+NSUFF_FEM_SG+CASE_INDEF_GEN",
"PREP",
"NOUN+CASE_DEF_GEN",
"NOUN+NSUFF_FEM_PL+CASE_DEF_GEN",
"POSS_PRON_3FS",
"DET+ADJ+NSUFF_FEM_SG+CASE_DEF_GEN",
"PREP",
"NOUN+CASE_DEF_GEN",
"DET+NOUN+CASE_DEF_GEN",
"PREP",
"NOUN+NSUFF_FEM_SG+CASE_DEF_GEN",
"PUNC",
"NOUN_PROP",
"NOUN_PROP",
"PUNC",
"REL_PRON",
"IV3FS+IV+IVSUFF_MOOD:I",
"IVSUFF_DO:3FS",
"NOUN+NSUFF_FEM_PL+CASE_INDEF_NOM",
"PREP",
"NOUN+CASE_DEF_GEN",
"PUNC",
"DET+NOUN_PROP+NSUFF_FEM_SG+CASE_DEF_GEN",
"PUNC",
"REL_PRON",
"IV3MS+IV+IVSUFF_MOOD:I",
"IVSUFF_DO:3MS",
"NOUN_PROP+NSUFF_FEM_SG",
"NOUN_PROP",
"NOUN_PROP",
"PUNC",
"PREP",
"REL_PRON",
"FUT_PART",
"IV3MS+IV+IVSUFF_MOOD:I",
"PREP",
"PRON_3MP",
"DET+NOUN+CASE_DEF_ACC",
"PREP",
"NOUN+CASE_DEF_GEN",
"DET+NOUN+CASE_DEF_GEN",
"PREP",
"NOUN+CASE_DEF_GEN",
"DET+NOUN+CASE_DEF_GEN",
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"I-ARG1",
"I-ARG1",
"I-ARG1",
"I-ARG1",
"O"
]
},
{
"verb": "{ِحْتَرَقَت#{iHotaraq#AHtrqt#{iHotaraq+at",
"frames": [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-ARG1",
"I-ARG1",
"B-V",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O"
]
},
{
"verb": "لَحِقَت#laHiq-a#lHqt#laHiq+at",
"frames": [
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"O",
"B-V",
"B-ARG1",
"I-ARG1",
"B-ARG0",
"O"
]
}
],
"coref_spans": [
[
11,
18,
18
],
[
11,
14,
15
]
]
},
{
"part_id": 0,
"words": [
"-LRB-#DEFAULT#(#(",
"و#wa#w#w",
"ص#S#S#S",
"ف#fa#f#f",
",#DEFAULT#,#,",
"رُويْتِرز#ruwyotir#rwytrz#ruwyotirz",
",#DEFAULT#,#,",
"أب#>b#>b#>b",
"-RRB-#DEFAULT#)#)"
],
"pos_tags": [
"PUNC",
"ABBREV",
"ABBREV",
"ABBREV",
"PUNC",
"NOUN_PROP",
"PUNC",
"ABBREV",
"PUNC"
],
"parse_tree": "(TOP(NP (PUNC -LRB-#DEFAULT#(#() (NP (ABBREV و#wa#w#w) (ABBREV ص#S#S#S) (ABBREV ف#fa#f#f) ) (PUNC ,#DEFAULT#,#,) (NP (NOUN_PROP رُويْتِرز#ruwyotir#rwytrz#ruwyotirz) ) (PUNC ,#DEFAULT#,#,) (NP (ABBREV أب#>b#>b#>b) ) (PUNC -RRB-#DEFAULT#)#)) ))",
"predicate_lemmas": [
"DEFAULT",
"wa",
"S",
"fa",
"DEFAULT",
"ruwyotir",
"DEFAULT",
">b",
"DEFAULT"
],
"predicate_framenet_ids": [
null,
null,
null,
null,
null,
null,
null,
null,
null
],
"word_senses": [
null,
null,
null,
null,
null,
null,
null,
null,
null
],
"speaker": null,
"named_entities": [
0,
0,
0,
0,
0,
7,
0,
0,
0
],
"srl_frames": [],
"coref_spans": []
}
] |
nw/ann/00/ann_0002 | [{"part_id":0,"words":["قَصَفَت#qaSaf-i#qSft#qaSaf+at","طائِراتٌ#TA}irap#TA}rAt#TA}ir(...TRUNCATED) |
nw/ann/00/ann_0003 | [{"part_id":0,"words":["الحُكُومَةُ#Hukuwmap#AlHkwmp#Al+Hukuwm+ap+u","الأُرْدُن(...TRUNCATED) |
nw/ann/00/ann_0004 | [{"part_id":0,"words":["بَدْءُ#bado'#bd'#bado'+u","مُحادَثاتٍ#muHAdavap#mHAdvAt#muHA(...TRUNCATED) |
nw/ann/00/ann_0005 | [{"part_id":0,"words":["بَرْلِين#baroliyn#brlyn#baroliyn","تَرْفُضُ#rafaD-u#trfD#ta+(...TRUNCATED) |
nw/ann/00/ann_0006 | [{"part_id":0,"words":["سَعُودِيُّونَ#saEuwdiy~#sEwdywn#saEuwdiy~+uwna","يَشْكُو(...TRUNCATED) |
nw/ann/00/ann_0007 | [{"part_id":0,"words":["مَساعٍ#masoEaY#msAEK#masAE+K","دِيبلُوماسِيَّةٌ#dibluw(...TRUNCATED) |
nw/ann/00/ann_0008 | [{"part_id":0,"words":["نِيُودِلْهِي#niyuwdilohiy#nywdlhy#niyuwdilohiy","أَكَّدَ(...TRUNCATED) |
nw/ann/00/ann_0011 | [{"part_id":0,"words":["تَضُمُّ#Dam~-u#tDm#ta+Dum~+u","27#DEFAULT#27#27","وَزِيراً#wa(...TRUNCATED) |
nw/ann/00/ann_0012 | [{"part_id":0,"words":["إِلَي-#<ilaY#<ly#<ilay-","-كُم#clitics#km#-kum","مِثالٌ#mivAl#(...TRUNCATED) |
End of preview. Expand
in Dataset Viewer.
Dataset Card for CoNLL2012 shared task data based on OntoNotes 5.0
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.
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Homepage:
Author's page
Repository:
Mendeley
Paper:
Towards Robust Linguistic Analysis using OntoNotes
Leaderboard:
Leaderboard:
Point of Contact:
Point of Contact:
Size of downloaded dataset files:
775 MB
Size of the auto-converted Parquet files:
79.3 MB
Number of rows:
17,669
