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Towards the Creation of AI-powered Queries Using Transfer Learning on NLP Model - The THESPIAN-NER Experience

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Image Analysis and Processing. ICIAP 2022 Workshops (ICIAP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13374))

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Abstract

Tools for HEritage Science Processing, Integration, and ANalysis (THESPIAN) is a cloud system that offers multiple web services to the researchers of INFN-CHNet, from storing their raw data to reusing them by following the FAIR principles for establishing integration and interoperability among shared information.

The injection in the CHNet cloud database of data and metadata (the latter modelled on a CIDOC-based ontology called CRMhs [20]) is performed by using the cloud service THESPIAN-Mask.

THESPIAN-NER is a tool based on a deep neural network for Named Entity Recognition (NER), which will ease the data extraction from the database, enabling users to upload .pdf or .txt files and obtain named entities and keywords to be fetched in the metadata entries of the database.

The neural network, on which THESPIAN-NER relies, is based on a set of open-source NLP models; transfer learning was employed to customise the Named Entity Recognition output of the models to match the CRMhs ontology properties.

The service is now available in alpha version to researchers on the CHNet cloud.

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Notes

  1. 1.

    For some references about the usage of NER in Cultural Heritage applications, see [8, 13, 15, 19] and references therein.

  2. 2.

    Using the open-source add-on library displacy, is possible to render the annotated text as HTML, where named entities are highlighted using a HEX colour. Such hex colour is employed in the web application as a visual aid to users, in order to visually discriminate entities among NER labels.

  3. 3.

    We recall that

    $$\begin{aligned} \mathrm {P} = \frac{TP}{TP+FP} \,, \quad \mathrm {R} = \frac{TP}{TP+FN} \,, \quad \mathrm {F} = 2 \, \frac{\mathrm {P} \cdot \mathrm {R}}{\mathrm {P} + \mathrm {R}} \,, \end{aligned}$$
    (1)

    where TP are the true positive counts, FP are false positive counts, and FN are false negative counts.

  4. 4.

    Using PyPDF2 for the .pdf parsing.

References

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Acknowledgments

The present research has been partially funded by the European Commission within the Framework Programme Horizon 2020 with the projects ARIADNEplus (GA no. H2020-INFRAIA-01-2018-2019-823914) and EOSC-Pillar (GA no. H2020-INFRAEOSC-05-2018-2019-857650).

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Authors and Affiliations

  1. INFN Florence Section, Via Bruno Rossi 1, Florence, Italy

    Alessandro Bombini, Lisa Castelli & Francesco Taccetti

  2. PIN, Prato, Italy

    Achille Felicetti, Franco Niccolucci & Anna Reccia

Authors
  1. Alessandro Bombini
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  2. Lisa Castelli
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  3. Achille Felicetti
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  4. Franco Niccolucci
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  5. Anna Reccia
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  6. Francesco Taccetti
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Correspondence to Alessandro Bombini .

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Editors and Affiliations

  1. National Research Council, Lecce, Italy

    Pier Luigi Mazzeo

  2. Università Politecnica delle Marche, Ancona, Italy

    Emanuele Frontoni

  3. Boston University, Boston, MA, USA

    Stan Sclaroff

  4. National Research Council, Lecce, Italy

    Cosimo Distante

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Bombini, A., Castelli, L., Felicetti, A., Niccolucci, F., Reccia, A., Taccetti, F. (2022). Towards the Creation of AI-powered Queries Using Transfer Learning on NLP Model - The THESPIAN-NER Experience. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13374. Springer, Cham. https://doi.org/10.1007/978-3-031-13324-4_23

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