End-to-End NLP Knowledge Graph Construction

Ishani Mondal; Yufang Hou; Charles Jochim

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arXiv · 2106.01167

End-to-End NLP Knowledge Graph Construction

Ishani Mondal·Yufang Hou·Charles Jochim

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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.

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Ishani Mondal, Yufang Hou, Charles Jochim. 2021-06-02. End-to-End NLP Knowledge Graph Construction. https://arxiv.org/abs/2106.01167

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