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Advanced Similarity Measures Using Word Embeddings and Siamese Networks in CBR

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Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1038))

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Abstract

Automatic fuzzy text processing, context extraction and disambiguation are three challenging research areas with high relevance to complex business domains. Business knowledge can be found in plain text message exchanges, emails, support tickets, internal chat messengers and other volatile means, making the decoding of text-based domain knowledge a challenging task. Traditional natural language processing approaches focus on a comprehensive representation of business knowledge and any relevant mappings. However, such approaches can be highly complex, not cost-effective and of high maintenance, especially in environments that experience frequent changes. This work applies LSTM Siamese Networks to measure text similarities in ambiguous domains. We implement the Manhattan LSTM (MaLSTM) Siamese neural network for semi-automatic knowledge acquisition of business knowledge and decoding of domain-relevant features that enable building similarity measures. Our aim is to minimize the effort from human experts while extracting domain knowledge from rich text, containing context-free abbreviations, grammatically incorrect text and mixed language.

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Author information

Authors and Affiliations

  1. Smart Data and Knowledge Services, German Research Center for Artificial Intelligence, Trippstadter Straße 122, 67663, Kaiserslautern, Germany

    Kareem Amin, Klaus-Dieter Althoff & Andreas Dengel

  2. Institute of Computer Science, Intelligent Information Systems Lab, University of Hildesheim, Hildesheim, Germany

    Klaus-Dieter Althoff

  3. Kaiserslautern University, P.O. Box 3049, 67663, Kaiserslautern, Germany

    Kareem Amin & Andreas Dengel

  4. School of Computing Engineering and Mathematics, University of Brighton, Brighton, UK

    Stelios Kapetanakis

  5. Department of Computing, University of Middlesex, London, UK

    Miltos Petridis

  6. School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths, University of Bristol, Bristol, UK

    George Lancaster

Authors
  1. Kareem Amin
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  2. George Lancaster
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  3. Stelios Kapetanakis
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  4. Klaus-Dieter Althoff
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  5. Andreas Dengel
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  6. Miltos Petridis
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Corresponding author

Correspondence to Kareem Amin .

Editor information

Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Amin, K., Lancaster, G., Kapetanakis, S., Althoff, KD., Dengel, A., Petridis, M. (2020). Advanced Similarity Measures Using Word Embeddings and Siamese Networks in CBR. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_32

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