Computer Science Named Entity Recognition in the Open Research Knowledge Graph

TL;DR

This paper introduces a standardized set of scholarly entities for Computer Science NER, compiles a large annotated dataset from research articles, and trains a neural model to improve scientific information extraction.

Contribution

It defines contribution-centric entities for CS NER, creates a large annotated dataset, and develops a neural sequence labeling model tailored for scholarly articles.

Findings

Created a large annotated corpus of CS scholarly entities

Proposed a standardized set of contribution-centric entities

Trained a neural NER model achieving promising results

Abstract

Domain-specific named entity recognition (NER) on Computer Science (CS) scholarly articles is an information extraction task that is arguably more challenging for the various annotation aims that can beset the task and has been less studied than NER in the general domain. Given that significant progress has been made on NER, we believe that scholarly domain-specific NER will receive increasing attention in the years to come. Currently, progress on CS NER -- the focus of this work -- is hampered in part by its recency and the lack of a standardized annotation aim for scientific entities/terms. This work proposes a standardized task by defining a set of seven contribution-centric scholarly entities for CS NER viz., research problem, solution, resource, language, tool, method, and dataset. Following which, its main contributions are: combines existing CS NER resources that maintain their annotation focus on the set or subset of contribution-centric scholarly entities we consider; further, noting the need for big data to train neural NER models, this work additionally supplies thousands of contribution-centric entity annotations from article titles and abstracts, thus releasing a cumulative large novel resource for CS NER; and, finally, trains a sequence labeling CS NER model inspired after state-of-the-art neural architectures from the general domain NER task. Throughout the work, several practical considerations are made which can be useful to information technology designers of the digital libraries.