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Application of Distance Measurement NLP Methods for Address and Location Matching in Logistics

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Intelligent Information and Database Systems: Recent Developments (ACIIDS 2019)

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Abstract

Paper is based on research based on linguistic terms denoting an address used for delivery services in logistic. Distance measurement NLP methods are widely usable in text mining and can be used to find the similarity among sentence or document. As part of logistics process, being able to determine correct address using machine learning we need to tackle issue of two addresses comparison (street name, city name etc.) is crucial for efficient service. This paper explains comparison techniques based on similarity score that can be calculated using distance measurement. As part of process, several distance measurements were compared while conclusion include results and recommendation on usage in address and location matching in logistics (post services).

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Authors and Affiliations

  1. Algebra University College, Ilica 242, Zagreb, Croatia

    Leo Mrsic

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  1. Leo Mrsic
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Correspondence to Leo Mrsic .

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Editors and Affiliations

  1. Department of Information Systems, Wrocław University of Science and Technology, Wrocław, Poland

    Maciej Huk

  2. Department of Information Systems, Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Maleszka

  3. Department of Management, Gdańsk University of Technology, Gdańsk, Poland

    Edward Szczerbicki

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Mrsic, L. (2020). Application of Distance Measurement NLP Methods for Address and Location Matching in Logistics. In: Huk, M., Maleszka, M., Szczerbicki, E. (eds) Intelligent Information and Database Systems: Recent Developments. ACIIDS 2019. Studies in Computational Intelligence, vol 830. Springer, Cham. https://doi.org/10.1007/978-3-030-14132-5_11

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