Datasets:
annotations_creators:
- expert-generated
language_creators:
- expert-generated
languages:
- en
- de
licenses:
- mit
multilinguality:
- multilingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-label-classification
- semantic-similarity-classification
pretty_name: SV-Ident
paperswithcode_id: sv-ident
Dataset Card for SV-Ident
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://vadis-project.github.io/sv-ident-sdp2022/
- Repository: https://github.com/vadis-project/sv-ident
- Paper: [Needs More Information]
- Leaderboard: [Needs More Information]
- Point of Contact: svident2022@googlegroups.com
Dataset Summary
SV-Ident comprises 4,248 sentences from social science publications in English and German. The data is the official data for the Shared Task: “Survey Variable Identification in Social Science Publications” (SV-Ident) 2022. Visit the homepage to find out more details about the shared task.
Supported Tasks and Leaderboards
The dataset supports:
Variable Detection: identifying whether a sentence contains a variable mention or not.
Variable Disambiguation: identifying which variable from a given vocabulary is mentioned in a sentence.
Languages
The text in the dataset is in English and German, as written by researchers. The domain of the texts is scientific publications in the social sciences.
Dataset Structure
Data Instances
{
"sentence": "Our point, however, is that so long as downward (favorable comparisons overwhelm the potential for unfavorable comparisons, system justification should be a likely outcome amongst the disadvantaged.",
"is_variable": 1,
"variable": ["exploredata-ZA5400_VarV66", "exploredata-ZA5400_VarV53"],
"research_data": ["ZA5400"],
"doc_id": "73106",
"uuid": "b9fbb80f-3492-4b42-b9d5-0254cc33ac10",
"lang": "en",
}
Data Fields
The following data fields are provided for documents:
sentence: Textual instance, which may contain a variable mention.
is_variable: Label, whether the textual instance contains a variable mention (1) or not (0). This column can be used for Task 1 (Variable Detection).
variable: Variables (separated by a comma ";") that are mentioned in the textual instance. This column can be used for Task 2 (Variable Disambiguation).
research_data: Research data IDs (separated by a ";") that are relevant for each instance (and in general for each "doc_id").
doc_id: ID of the source document. Each document is written in one language (either English or German).
uuid: Unique ID of the instance in uuid4 format.
lang: Language of the sentence.
Data Splits
| Split | Number of sentences |
|---|---|
| Train | 4,248 |
Dataset Creation
Curation Rationale
The dataset was curated by the VADIS project (https://vadis-project.github.io/). The documents were annotated by two expert annotators.
Source Data
Initial Data Collection and Normalization
The original data are available at GESIS (https://www.gesis.org/home) in an unprocessed format.
Who are the source language producers?
[Needs More Information]
Annotations
Annotation process
[Needs More Information]
Who are the annotators?
The documents were annotated by two expert annotators.
Personal and Sensitive Information
The dataset does not include personal or sensitive information.
Considerations for Using the Data
Social Impact of Dataset
[Needs More Information]
Discussion of Biases
[Needs More Information]
Other Known Limitations
[Needs More Information]
Additional Information
Dataset Curators
VADIS project (https://vadis-project.github.io/)
Licensing Information
[Needs More Information]
Citation Information
@misc{sv-ident,
author={vadis-project},
title={SV-Ident},
year={2022},
url={https://github.com/vadis-project/sv-ident},
}
Contributions
[Needs More Information]