Datasets:
File size: 22,908 Bytes
1b317da afcdcc7 1b317da afcdcc7 3eae3a6 1b317da da10afc 1b317da d297875 1b317da 1b62edf f45b3ca 5f12f57 1b62edf 5f12f57 1b62edf 5f12f57 dc0d2a2 5f12f57 1b62edf 5f12f57 1b62edf 5f12f57 dc0d2a2 5f12f57 1b62edf 5f12f57 dc0d2a2 5f12f57 1b62edf 5f12f57 1b62edf 5f12f57 dc0d2a2 5f12f57 1b317da f45b3ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 |
---
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': [],
}
```
### Data Fields
- **`document_id`** (*`str`*): This is a variation on the document filename
- **`sentences`** (*`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]` 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.
- 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)
- 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",])`, where `X` is 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",])`, where `XX` is 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",])`, where `XX` is for pos tag missing, and `-LRB-`/`-RRB-` is "`(`" / "`)`".
- **`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`.
- **`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`.
- **`predicate_framenet_ids`** (*`List[Optional[int]]`*) : The PropBank frameset ID of the lemmas in predicate_lemmas, or `None`.
- **`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 be `None`.
- **`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",])`
- **`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](https://github.com/richarddwang) for adding this dataset. |