Datasets:
The viewer is disabled because this dataset repo requires arbitrary Python code execution. Please consider
removing the
loading script
and relying on
automated data support
(you can use
convert_to_parquet
from the datasets
library). If this is not possible, please
open a discussion
for direct help.
Dataset Card for "gids"
Dataset Summary
The Google-IISc Distant Supervision (GIDS) is a new dataset for distantly-supervised relation extraction. GIDS is seeded from the human-judged Google relation extraction corpus. See the paper for full details: Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention
Note:
- There is a formatted version that you can load with
datasets.load_dataset('gids', name='gids_formatted')
. This version is tokenized with spaCy, removes the underscores in the entities and provides entity offsets.
Supported Tasks and Leaderboards
- Tasks: Relation Classification
- Leaderboards: More Information Needed
Languages
The language in the dataset is English.
Dataset Structure
Data Instances
gids
- Size of downloaded dataset files: 8.94 MB
- Size of the generated dataset: 8.5 MB An example of 'train' looks as follows:
{
"sentence": "War as appropriate. Private Alfred James_Smurthwaite Sample. 26614. 2nd Battalion Yorkshire Regiment. Son of Edward James Sample, of North_Ormesby , Yorks. Died 2 April 1917. Aged 29. Born Ormesby, Enlisted Middlesbrough. Buried BUCQUOY ROAD CEMETERY, FICHEUX. Not listed on the Middlesbrough War Memorial Private Frederick Scott. 46449. 4th Battalion Yorkshire Regiment. Son of William and Maria Scott, of 25, Aspinall St., Heywood, Lancs. Born at West Hartlepool. Died 27 May 1918. Aged 24.",
"subj_id": "/m/02qt0sv",
"obj_id": "/m/0fnhl9",
"subj_text": "James_Smurthwaite",
"obj_text": "North_Ormesby",
"relation": 4
}
gids_formatted
- Size of downloaded dataset files: 8.94 MB
- Size of the generated dataset: 11.82 MB An example of 'train' looks as follows:
{
"token": ["announced", "he", "had", "closed", "shop", ".", "Mary", "D.", "Crisp", "Coyle", "opened", "in", "1951", ".", "Stoffey", ",", "a", "Maricopa", "County", "/", "Phoenix", "city", "resident", "and", "longtime", "customer", ",", "bought", "the", "business", "in", "2011", ",", "when", "then", "owners", "were", "facing", "closure", ".", "He", "renovated", "the", "diner", "is", "interior", ",", "increased", "training", "for", "staff", "and", "expanded", "the", "menu", "."],
"subj_start": 6,
"subj_end": 9,
"obj_start": 17,
"obj_end": 22,
"relation": 4
}
Data Fields
The data fields are the same among all splits.
gids
sentence
: the sentence, astring
feature.subj_id
: the id of the relation subject mention, astring
feature.obj_id
: the id of the relation object mention, astring
feature.subj_text
: the text of the relation subject mention, astring
feature.obj_text
: the text of the relation object mention, astring
feature.relation
: the relation label of this instance, anint
classification label.
{"NA": 0, "/people/person/education./education/education/institution": 1, "/people/person/education./education/education/degree": 2, "/people/person/place_of_birth": 3, "/people/deceased_person/place_of_death": 4}
gids_formatted
token
: the list of tokens of this sentence, obtained with spaCy, alist
ofstring
features.subj_start
: the 0-based index of the start token of the relation subject mention, anìnt
feature.subj_end
: the 0-based index of the end token of the relation subject mention, exclusive, anìnt
feature.obj_start
: the 0-based index of the start token of the relation object mention, anìnt
feature.obj_end
: the 0-based index of the end token of the relation object mention, exclusive, anìnt
feature.relation
: the relation label of this instance, anint
classification label.
{"NA": 0, "/people/person/education./education/education/institution": 1, "/people/person/education./education/education/degree": 2, "/people/person/place_of_birth": 3, "/people/deceased_person/place_of_death": 4}
Data Splits
Train | Dev | Test | |
---|---|---|---|
GIDS | 11297 | 1864 | 5663 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
Citation Information
@article{DBLP:journals/corr/abs-1804-06987,
author = {Sharmistha Jat and
Siddhesh Khandelwal and
Partha P. Talukdar},
title = {Improving Distantly Supervised Relation Extraction using Word and
Entity Based Attention},
journal = {CoRR},
volume = {abs/1804.06987},
year = {2018},
url = {http://arxiv.org/abs/1804.06987},
eprinttype = {arXiv},
eprint = {1804.06987},
timestamp = {Fri, 15 Nov 2019 17:16:02 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1804-06987.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Contributions
Thanks to @phucdev for adding this dataset.
- Downloads last month
- 48