---
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},
}

```