@inproceedings{mondal2021end-to-end,
author = {Mondal, Ishani and Hou, Yufang and Jochim, Charles},
title = {End-to-End NLP Knowledge Graph Construction},
booktitle = {ACL-IJCNLP 2021},
year = {2021},
month = {June},
abstract = {This paper studies the end-to-end construction of an NLP Knowledge Graph (KG) from scientific papers. We focus on extracting four types of relations: evaluatedOn between tasks and datasets, evaluatedBy between tasks and evaluation metrics, as well as coreferent and related relations between the same type of entities. For instance, F1-score is coreferent with F-measure. We introduce novel methods for each of these relation types and apply our final framework (SciNLP-KG) to 30,000 NLP papers from ACL Anthology to build a large-scale KG, which can facilitate automatically constructing scientific leaderboards for the NLP community. The results of our experiments indicate that the resulting KG contains high-quality information.},
url = {http://approjects.co.za/?big=en-us/research/publication/end-to-end-nlp-knowledge-graph-construction/},
}