<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis.201606_s_006_w2aab2b8c73b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>Our study proposes a bootstrapping-based method to automatically extract data-usage statements from academic texts.</p></sec><sec id="j_jdis.201606_s_007_w2aab2b8c73b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>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.</p></sec><sec id="j_jdis.201606_s_008_w2aab2b8c73b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>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.</p></sec><sec id="j_jdis.201606_s_009_w2aab2b8c73b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>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.</p></sec><sec id="j_jdis.201606_s_010_w2aab2b8c73b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>Data-usage statements extraction is beneficial for data-repository construction and facilitates research on data-usage tracking, dataset-based scholar search, and dataset evaluation.</p></sec><sec id="j_jdis.201606_s_011_w2aab2b8c73b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>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.</p></sec></abstract>

PurposeOur study proposes a bootstrapping-based method to automatically extract data-usage statements from academic texts.Design/methodology/approachThe 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.FindingsThe 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 limitationsWhile 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 implicationsData-usage statements extraction is beneficial for data-repository construction and facilitates research on data-usage tracking, dataset-based scholar search, and dataset evaluation.Originality/valueTo 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.

A Bootstrapping-based Method to Automatically Identify Data-usage Statements in Publications

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

PurposeOur study proposes a bootstrapping-based method to automatically extract data-usage statements from academic texts.Design/methodology/approachThe method...

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
Data-usage statements extraction, Information extraction, Bootstrapping, Unsupervised learning, Academic text-mining

© 2016 Qiuzi Zhang, Qikai Cheng, Yong Huang, Wei Lu

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

SchlüsselwörterData-usage statements extraction, Information extraction, Bootstrapping, Unsupervised learning, Academic text-mining

© 2016 Qiuzi Zhang, Qikai Cheng, Yong Huang, Wei Lu

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Data-usage statements extraction, Information extraction, Bootstrapping, Unsupervised learning, Academic text-mining