Natural Language Processing Using Kepler Workflow System: First Steps

Goyal, Ankit; Singh, Alok; Bhargava, Shitij; Crawl, Daniel; Altıntaş, Ilkay; Hsu, Chun‐Nan

doi:10.1016/j.procs.2016.05.358

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…In the future, we plan to run each of the text sources in parallel further improving the overall performance, for which our initial result was reported in (Goyal et al 2016). Also, we plan to explore more challenging issues in the PDF to XML conversion process.…”

Section: Discussionmentioning

confidence: 99%

bioPDFX: preparing PDF scientific articles for biomedical text mining

Bhargava

Kuo

Goyal

et al. 2017

Preprint

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Background. There is huge amount of full-text biomedical literatures available in public repositories like PubMed Central (PMC). However, a substantial number of the papers are in Portable Document Format (PDF) and do not provide plain text format ready for text mining and natural language processing (NLP). Although there exist many PDF-to-text converters, they still suffer from several challenges while processing biomedical PDFs, such as the correct transcription of titles/abstracts, segmenting references/acknowledgements, special characters, jumbling errors (the wrong order of the text), and word boundaries. Methods. In this paper, we present bioPDFX, a novel tool which complements weaknesses with strengths of multiple state-of-the-art methods and then applies machine learning methods to address all issues above Results. The experiment results on publications of Genome Wide Association Studies (GWAS) demonstrated that bioPDFX significantly improved the quality of XML comparing to state-of-the-art PDF-to-XML converter, leading to a biomedical database more suitable for text mining. Discussion. Overall, the whole pipeline developed in this paper makes the published literature in form of PDF files much better suited for text mining tasks, while slightly improving the overall text quality as well. The service is open to access freely at URL: http://textmining.ucsd.edu:9000 . A list of PubMed Central IDs of the 941 articles (see Supplemental File 1) used in this study is available for download at the same URL. The instructions of how to run the service with a PubMed ID are described in Supplemental File 2.

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Section: Discussionmentioning

confidence: 99%

bioPDFX: preparing PDF scientific articles for biomedical text mining

Bhargava

Kuo

Goyal

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…• Jumbling error correction is only an approximation since we fix n=5 581 In the future, we plan to run each of the text sources in parallel further improving the overall 583 performance, for which our initial result was reported in (Goyal et al 2016). Also, we plan to 584 explore more challenging issues in the PDF to XML conversion process.…”

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confidence: 99%