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---
license: cc-by-nc-2.0
task_categories:
- token-classification
language:
- en
multilinguality:
- monolingual
tags:
- architecture
- engineering
- construction
- operations
- aeco
- scierc
- Scientific Knowledge Graph
- NER
- RE
- Relation
- Extraction
size_categories:
- 1K<n<10K
source_datasets:
- original
dataset_info:
features:
- name: sentence_text
dtype: string
- name: Tasks
dtype: string
- name: Methods
dtype: string
- name: Metrics
dtype: string
- name: USED-FOR
dtype: string
- name: EVALUATE-FOR
dtype: string
- name: relevant
dtype: string
splits:
- name: train
num_examples: 1016
- name: test
num_examples: 200
---
# Dataset Card for SciERC AECO dataset
## 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:**
- **Paper:** [A Few-Shot Approach for Relation Extraction Domain Adaptation using Large Language Models](https://arxiv.org/abs/2408.02377)
- **Point of Contact:** [Vanni Zavarella](mailto:vanni.zavarella@unica.it)
### Dataset Summary
The SciERC AECO dataset is an English-language dataset containing 1016 sentences from research papers in the AECO domain, annotated for scientific entities and relations based on the SciERC annotation schema.
### Supported Tasks and Leaderboards
- 'NER': the dataset can be used to train a model to detect scientific entities according to the SciERC annotation schema
- 'Relation extraction': the dataset can be used to train a model to detect binary relations between pairs of scientific entities according to the SciERC annotation schema.
### Languages
English (EN)
## Dataset Structure
### Data Instances
Each row in the dataset contains:
- a "sentence_text" string valued attribute
- an (optionally empty) "Tasks" attribute containing annotated entities of type TASK, in the form of a string dictionary "Ti":"entity_string" where Ti is an index of the entity and "entity_string" the string match of the entity in the sentence_text
- an (optionally empty) "Methods" attribute containing annotated entities of type METHOD, in the form of a string dictionary "Ti":"entity_string" where Ti is an index of the entity and "entity_string" the string match of the entity in the sentence_text
- an (optionally empty) "Metrics" attribute containing annotated entities of type METRIC, in the form of a string dictionary "Ti":"entity_string" where Ti is an index of the entity and "entity_string" the string match of the entity in the sentence_text
- an (optionally empty) "USED-FOR" attribute containing annotated relations in the form of a list of dictionaries "Ti":"Tj" where Ti is the index of the subject entity and Tj is the index of the object entity
- an (optionally empty) "EVALUATE-FOR" attribute containing annotated relations in the form of a list of dictionaries "Ti":"Tj" where Ti is the index of the subject entity and Tj is the index of the object entity
- a boolean attribute "relevant" marking if any of the "Tasks","Methods" or "Metrics" attributes is non-emtpy ("relevant"=True), meaning that the sentence contains True positive examples of SciERC entity and/or relations
### Data Fields
Each row in the dataset contains:
- a "sentence_text" string valued attribute
- an (optionally empty) "Tasks" attribute containing annotated entities of type TASK, in the form of a string dictionary "Ti":"entity_string" where Ti is an index of the entity and "entity_string" the string match of the entity in the sentence_text
- an (optionally empty) "Methods" attribute containing annotated entities of type METHOD, in the form of a string dictionary "Ti":"entity_string" where Ti is an index of the entity and "entity_string" the string match of the entity in the sentence_text
- an (optionally empty) "Metrics" attribute containing annotated entities of type METRIC, in the form of a string dictionary "Ti":"entity_string" where Ti is an index of the entity and "entity_string" the string match of the entity in the sentence_text
- an (optionally empty) "USED-FOR" attribute containing annotated relations in the form of a list of dictionaries "Ti":"Tj" where Ti is the index of the subject entity and Tj is the index of the object entity
- an (optionally empty) "EVALUATE-FOR" attribute containing annotated relations in the form of a list of dictionaries "Ti":"Tj" where Ti is the index of the subject entity and Tj is the index of the object entity
- a boolean attribute "relevant" marking if any of the "Tasks","Methods" or "Metrics" attributes is non-emtpy ("relevant"=True), meaning that the sentence contains True positive examples of SciERC entity and/or relations
## Dataset Creation
### Source Data
#### Initial Data Collection
The source data comprise titles and abstracts from a collection of research articles in the AECO area published in the time range 2010-2023, retrieved from the OpenAlex2 open scientific graph database (https://docs.openalex.org) using a set of platform-specific topic filtering tags.
#### Who are the annotators?
Vanni Zavarella
Juan Carlos Gamero Salinas
### Personal and Sensitive Information
No personal/sensitive information is included.
## Considerations for Using the Data
### Licensing Information
The SciERC AECO dataset is released under the [cc-by-nc-2.0].
### Citation Information
```
@misc{zavarella2024fewshotapproachrelationextraction,
title={A Few-Shot Approach for Relation Extraction Domain Adaptation using Large Language Models},
author={Vanni Zavarella and Juan Carlos Gamero-Salinas and Sergio Consoli},
year={2024},
eprint={2408.02377},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2408.02377},
}
```
```
@InProceedings{luan2018multitask,
author = {Luan, Yi and He, Luheng and Ostendorf, Mari and Hajishirzi, Hannaneh},
title = {Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction},
booktitle = {Proc.\ Conf. Empirical Methods Natural Language Process. (EMNLP)},
year = {2018},
}
```
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