A Bootstrapping-based Method to Automatically Identify Data-usage Statements in Publications
Article Category: Research Paper
Online veröffentlicht: 01. Sept. 2017
Seitenbereich: 69 - 85
Eingereicht: 21. Jan. 2016
Akzeptiert: 26. Feb. 2016
DOI: https://doi.org/10.20309/jdis.201606
Schlüsselwörter
© 2016 Qiuzi Zhang, Qikai Cheng, Yong Huang, Wei Lu
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
Purpose
Our study proposes a bootstrapping-based method to automatically extract data-usage statements from academic texts.
Design/methodology/approach
The method for data-usage statements extraction starts with seed entities and iteratively learns patterns and data-usage statements from unlabeled text. In each iteration, new patterns are constructed and added to the pattern list based on their calculated score. Three seed-selection strategies are also proposed in this paper.
Findings
The performance of the method is verified by means of experiments on real data collected from computer science journals. The results show that the method can achieve satisfactory performance regarding precision of extraction and extensibility of obtained patterns.
Research limitations
While the triple representation of sentences is effective and efficient for extracting data-usage statements, it is unable to handle complex sentences. Additional features that can address complex sentences should thus be explored in the future.
Practical implications
Data-usage statements extraction is beneficial for data-repository construction and facilitates research on data-usage tracking, dataset-based scholar search, and dataset evaluation.
Originality/value
To the best of our knowledge, this paper is among the first to address the important task of automatically extracting data-usage statements from real data.