Publication
ACL-IJCNLP 2021
Conference paper

End-to-End Construction of NLP Knowledge Graph

ACL-IJCNLP 2021

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.

Date

01 Aug 2021

Publication

ACL-IJCNLP 2021

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