metadata
dataset_info:
features:
- name: id
dtype: string
- name: words
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-abstract
'2': I-abstract
'3': B-animal
'4': I-animal
'5': B-event
'6': I-event
'7': B-object
'8': I-object
'9': B-organization
'10': I-organization
'11': B-person
'12': I-person
'13': B-place
'14': I-place
'15': B-plant
'16': I-plant
'17': B-quantity
'18': I-quantity
'19': B-substance
'20': I-substance
'21': B-time
'22': I-time
splits:
- name: train
num_bytes: 754935
num_examples: 2495
- name: test
num_bytes: 311483
num_examples: 1000
download_size: 296638
dataset_size: 1066418
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- token-classification
language:
- en
size_categories:
- 1K<n<10K
GUM: The Georgetown University Multilayer Corpus
The GUM corpus was collected and annotated at Georgetown University. For more information, see the LICENSE.
Structure
- Number of labels: 23
['O',
'B-abstract', 'I-abstract',
'B-animal', 'I-animal',
'B-event', 'I-event',
'B-object', 'I-object',
'B-organization', 'I-organization',
'B-person', 'I-person',
'B-place', 'I-place',
'B-plant', 'I-plant',
'B-quantity', 'I-quantity',
'B-substance', 'I-substance',
'B-time', 'I-time']
Train set
Number of sentences in the train set: 2495
Label count in train set:
| Label | Count |
|---|---|
| O | 20460 |
| I-abstract | 4687 |
| I-event | 2707 |
| I-place | 2212 |
| B-abstract | 2002 |
| B-person | 1920 |
| I-person | 1866 |
| I-object | 1732 |
| B-place | 1150 |
| B-object | 1017 |
| B-event | 738 |
| I-time | 663 |
| I-organization | 552 |
| I-substance | 458 |
| B-time | 401 |
| B-organization | 397 |
| B-substance | 278 |
| I-quantity | 203 |
| I-plant | 166 |
| B-plant | 144 |
| B-animal | 141 |
| I-animal | 120 |
| B-quantity | 97 |
Test set
Number of sentences in the test set: 1000
Label count in test set:
| Label | Count |
|---|---|
| O | 8543 |
| I-abstract | 2048 |
| I-event | 934 |
| I-place | 926 |
| B-person | 823 |
| B-abstract | 798 |
| I-object | 782 |
| I-person | 685 |
| B-place | 469 |
| B-object | 420 |
| B-event | 315 |
| I-organization | 278 |
| I-time | 242 |
| B-organization | 192 |
| I-substance | 183 |
| B-time | 179 |
| B-substance | 95 |
| I-quantity | 77 |
| B-plant | 62 |
| I-plant | 56 |
| B-quantity | 44 |
| I-animal | 43 |
| B-animal | 42 |
Citation
@Article{Zeldes2017,
author = {Amir Zeldes},
title = {The {GUM} Corpus: Creating Multilayer Resources in the Classroom},
journal = {Language Resources and Evaluation},
year = {2017},
volume = {51},
number = {3},
pages = {581--612},
doi = {http://dx.doi.org/10.1007/s10579-016-9343-x}
}