metadata

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

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage:
Paper: A Few-Shot Approach for Relation Extraction Domain Adaptation using Large Language Models
Point of Contact: Vanni Zavarella

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},
}

Datasets:

zavavan
/

scierc_aeco