A Framework to Automatically Extract Funding Information from Text

Kayal, Smita; Afzal, Zubair; Tsatsaronis, George; Doornenbal, Marius A.; Katrenko, Sophia; Gregory, Michelle

doi:10.1007/978-3-030-13709-0_27

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…Text mining methods are also employed to extract metadata from published articles. Kayal et al [220] developed a method to automatically extract funding information from scientific articles. Yousif et al [221] developed a model based on deep learning to extract the purpose of a citation to an article.…”

Section: Published Articlesmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

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Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

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Section: Published Articlesmentioning

confidence: 99%

Text Mining in Big Data Analytics

Hassani

Beneki

Unger

et al. 2020

BDCC

205

View full text Add to dashboard Cite

show abstract

“…The system achieves a micro-F 1 up to 0.90 in extracting grant pairs (agency, number). Kayal et al (2019) proposed an ensemble approach called FUNDINGFINDER for extracting funding information from text. The authors construct feature vectors for candidate entities using whether the entities are recognized by four NER implementation: Stanford (Conditional Random Field model), LingPipe (Hidden Markov model), OpenNLP (Maximum Entropy model), and Elsevier's Fingerprint Engine.…”

Section: Related Workmentioning

confidence: 99%

Acknowledgement Entity Recognition in CORD-19 Papers

Wang²,

Wei

et al. 2020

Proceedings of the First Workshop on Scholarly Document Processing

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Acknowledgements are ubiquitous in scholarly papers. Existing acknowledgement entity recognition methods assume all named entities are acknowledged. Here, we examine the nuances between acknowledged and named entities by analyzing sentence structure. We develop an acknowledgement extraction system, ACKEXTRACT based on open-source text mining software and evaluate our method using manually labeled data. ACKEXTRACT uses the PDF of a scholarly paper as input and outputs acknowledgement entities. Results show an overall performance of F 1 = 0.92. We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by ACKEXTRACT including persons and organizations and find that only up to 50-60% of named entities are actually acknowledged. We further analyze chronological trends of acknowledgement entities in CORD-19 papers. All codes and labeled data are publicly available at https://github.com/ lamps-lab/ackextract.

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A Framework to Automatically Extract Funding Information from Text

Cited by 2 publications

References 9 publications

Text Mining in Big Data Analytics

Text Mining in Big Data Analytics

Acknowledgement Entity Recognition in CORD-19 Papers

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