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Improving NER Performance by Applying Text Summarization on Pharmaceutical Articles

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ICT Innovations 2020. Machine Learning and Applications (ICT Innovations 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1316))

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Abstract

Analyzing long text articles in the pharmaceutical domain, for the purpose of knowledge extraction and recognizing entities of interest, is a tedious task. In our previous research efforts, we were able to develop a platform which successfully extracts entities and facts from pharmaceutical texts and populates a knowledge graph with the extracted knowledge. However, one drawback of our approach was the processing time; the analysis of a single text source was not interactive enough, and the batch processing of entire article datasets took too long. In this paper, we propose a modified pipeline where the texts are summarized before the analysis begins. With this, the source articles is reduced significantly, to a compact version which contains only the most commonly encountered entities. We show that by reducing the text size, we get knowledge extraction results comparable to the full text analysis approach and, at the same time, we significantly reduce the processing time, which is essential for getting both real-time results on single text sources, and faster results when analyzing entire batches of collected articles from the domain.

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Notes

  1. 1.

    https://github.com/f-data/NER_Pharma.

  2. 2.

    https://github.com/summanlp/gensim.

  3. 3.

    https://github.com/huggingface/neuralcoref.

  4. 4.

    The articles were retrieved from https://www.fiercepharma.com/, https://www.pharmacist.com/ and https://www.pharmaceutical-journal.com/.

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Acknowledgement

The work in this paper was partially financed by the Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North Macedonia

    Jovana Dobreva, Nasi Jofche, Milos Jovanovik & Dimitar Trajanov

Authors
  1. Jovana Dobreva
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  2. Nasi Jofche
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  3. Milos Jovanovik
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  4. Dimitar Trajanov
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Corresponding author

Correspondence to Milos Jovanovik .

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Editors and Affiliations

  1. University Ss. Cyril and Methodius, Skopje, North Macedonia

    Vesna Dimitrova

  2. University Ss. Cyril and Methodius, Skopje, North Macedonia

    Ivica Dimitrovski

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Dobreva, J., Jofche, N., Jovanovik, M., Trajanov, D. (2020). Improving NER Performance by Applying Text Summarization on Pharmaceutical Articles. In: Dimitrova, V., Dimitrovski, I. (eds) ICT Innovations 2020. Machine Learning and Applications. ICT Innovations 2020. Communications in Computer and Information Science, vol 1316. Springer, Cham. https://doi.org/10.1007/978-3-030-62098-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-62098-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62097-4

  • Online ISBN: 978-3-030-62098-1

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