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From Automatic Keyword Detection to Ontology-Based Topic Modeling

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Book cover Document Analysis Systems (DAS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12116))

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Abstract

In this paper, we propose a novel, two-staged system, for keyword detection and ontology-driven topic modeling. The first stage specializes in keyword detection in which we introduce a novel graph-based unsupervised approach called Collective Connectivity-Aware Node Weight (CoCoNoW) for detecting keywords from the scientific literature. CoCoNoW builds a connectivity aware graph from a given publication text and eventually assigns weight to the extracted keywords to sort them in order of relevance. The second stage specializes in topic modeling, where a domain ontology serves as an attention-map/context for topic modeling based on the detected keywords. The use of an ontology makes this approach independent of domain and language. CoCoNoW is extensively evaluated on three publicly available datasets Hulth2003, NLM500 and SemEval2010. Analysis of results reveals that CoCoNoW consistently outperforms the state-of-the-art approaches on the respective datasets.

M. Beck, S.T.R. Rizvi—Equal contribution.

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Notes

  1. 1.

    https://cso.kmi.open.ac.uk, accessed Dec-2019.

  2. 2.

    http://www.lextek.com/manuals/onix/stopwords2.html, accessed Dec 2019.

  3. 3.

    https://cso.kmi.open.ac.uk, accessed Dec-2019.

  4. 4.

    https://www.w3.org/2004/02/skos, accessed Dec-2019.

  5. 5.

    https://icdar2019.org/call-for-papers, accessed Dec-2019.

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Author information

Authors and Affiliations

  1. Technische Universität Kaiserslautern, Kaiserslautern, Germany

    Marc Beck, Syed Tahseen Raza Rizvi & Andreas Dengel

  2. German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany

    Syed Tahseen Raza Rizvi, Andreas Dengel & Sheraz Ahmed

Authors
  1. Marc Beck
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  2. Syed Tahseen Raza Rizvi
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  3. Andreas Dengel
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  4. Sheraz Ahmed
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Corresponding author

Correspondence to Syed Tahseen Raza Rizvi .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Xiang Bai

  2. Autonomous University of Barcelona, Barcelona, Spain

    Dimosthenis Karatzas

  3. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

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Beck, M., Rizvi, S.T.R., Dengel, A., Ahmed, S. (2020). From Automatic Keyword Detection to Ontology-Based Topic Modeling. In: Bai, X., Karatzas, D., Lopresti, D. (eds) Document Analysis Systems. DAS 2020. Lecture Notes in Computer Science(), vol 12116. Springer, Cham. https://doi.org/10.1007/978-3-030-57058-3_32

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  • DOI: https://doi.org/10.1007/978-3-030-57058-3_32

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