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Unsupervised Classification of Medical Documents Through Hybrid MWEs Discovery

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Advanced Information Networking and Applications (AINA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 227))

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Abstract

The automatic processing of medical language represents a clue for computational linguists due to intrinsic feature of these sub-codes: its lexicon comprises a vast number of terms that appear infrequently in texts. In addition, the presence of many sub-domains that can coincide in a single text complicates the collection of the training set for a supervised classification task. This paper will tackle the problem of unsupervised classification of medical scientific papers based on a hybrid Multiword Expression Discovery. We apply a morpho-semantic approach to extract medical domain terms and their semantic tags in addition to the classic MWEs discovery strategies. The collected MWEs will be used to vectorize texts and generate a network of similarities among corpus documents. With this approach, we try to solve both problems caused by the medical domain features. The presence of a vast lexicon of low-frequency terms is dealt with by extracting many semantic tags with a small dictionary; the issues of co-occurring sub-domains are solved by generating clusters of similarity values instead of a rigid classification.

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Notes

  1. 1.

    This paper has been produced with the financial support of the Project financed by Campania Region of Italy ‘REMIAM - Rete Musei intelligenti ad avanzata Multimedialita’. CUP B63D18000360007.

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Authors and Affiliations

  1. University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, (SA), Italy

    Alessandro Maisto

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  1. Alessandro Maisto
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Correspondence to Alessandro Maisto .

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Department of Computer Science, Ryerson University, Toronto, ON, Canada

    Isaac Woungang

  3. Faculty of Business Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Maisto, A. (2021). Unsupervised Classification of Medical Documents Through Hybrid MWEs Discovery. In: Barolli, L., Woungang, I., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2021. Lecture Notes in Networks and Systems, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-030-75078-7_6

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