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Potential Use-Cases of Natural Language Processing for a Logistics Organization

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Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough

Part of the book series: Studies in Computational Intelligence ((SCI,volume 956))

Abstract

All industries like Healthcare and Medicine, Education, Marketing, e-commerce are using AI and providing a technical advantage to these industries. Logistics is such an industry where AI has started showing its effect by making SCM a more seamless process. Processing natural language has always been a computer science and AI subfield, which covers interactions between computer and human language. The existing literature review lacks in representing the recent developments and challenges of NLP to maintain a competitive edge in the field of logistics. Literature Survey also shows that many of us are curious about knowing the various scopes of implementing NLP in Logistics. This article aims to answer the question by exploring the use-cases, challenges, and approaches of NLP in logistics. This study is of corresponding interest to researchers and practitioners. The study demonstrates a deeper understanding of logistics tasks similarly by implementing NLP approaches.

Supported by ATA Freight Line India Pvt. Ltd.

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Acknowledgements

The author thanks, Akshay Ghodake for valuable discussion on Natural Language Processing and Logistics in general. This research is supported by ATA Freight Line Pvt. Ltd. Research Fellowship.

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Authors and Affiliations

  1. Dr. Babasaheb Ambedkar Technological University, Lonere, MS, India

    Rachit Garg, Arvind W. Kiwelekar & Laxman D. Netak

  2. ATA Freight Line India Pvt. Ltd., Pune, MS, India

    Swapnil S. Bhate

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  1. Rachit Garg
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  2. Arvind W. Kiwelekar
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  4. Swapnil S. Bhate
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Correspondence to Rachit Garg .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, CMR Institute of Technology, Hyderabad, India

    Vinit Kumar Gunjan

  2. Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

Ethics declarations

Arvind W. Kiwelekary and Laxman D. Netak declares that they have no conflict of interest. Rachit Garg has received research grants from ATA Freight Line India Pvt. Ltd. Swapnil S. Bhate owns a position of Innovation Associate in CATI department of ATA Freight Line India Pvt. Ltd.

Glossary

Glossary

  • AI (Artificial Intelligence)—AI is an area of study in the computer science domain that automates the intuitive behavior of machines and smart software.

  • ANN (Artificial Neural Networks)—The ANN consists of several nodes like the human brain and trying to mimic the behavior of its biological cells.

  • CBOW—It stands for Continuous Bag-of-Words. This method attempts to determine the present target word based on the surrounding words.

  • CPA (Cognitive Procurement Advisor)—It is a method of using innovative technologies to help in the management of the procurement. It uses self-learning technology to handle data and assist in the procurement or purchase of products and services.

  • HMM (Hidden Markov Models)—HMMs are a class of probabilistic graphic models, which allows a series of unknown (hidden) variables from the observed variables to projected. A simple example is the weather forecast (hidden variable), depending on the type of clothing that someone wears (observed).

  • NER (Named Entity Recognition)—The purpose of NER is to recognize and classify named entities into pre-defined categories in text, including names of persons, organizations, places, terms of time, numbers, monetary values, percentages.

  • NLP—It stands for Natural Language Processing. It is a method of analyzing, perceiving, and deriving context smartly and sensibly from human speech.

  • POS—It stands for Parts-of-Speech. It allocates the syntactic functions of a word to a group. The main components in the language are the words noun, pronoun, adjective, determinant, verb, adverb, and preposition.

  • RAKE (Rapid Automatic Keyword Extraction)—We use RAKE for keyword extraction and keyword ranking from a document.

  • RNN—It stands for Recurrent Neural Network. It is a neural network where the output from the previous step is passed as input to the current level.

  • TF-IDF—It is a measure of the importance of a word. It stands for Term Frequency—Inverse Document Frequency. Term Frequency is the proportion of how as often as possible, the term ‘T’ appears in a ‘D’ document. Document Frequency is the appearance of the term ‘T’ in all document sets. IDF is inverse of document frequency and measures how much information that word gives. The multiplication of these two measures comes out as the measure of TF-IDF.

  • VPA is a software program that understands natural language voice commands and completes user tasks, also known as an AI assistant.

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Garg, R., Kiwelekar, A.W., Netak, L.D., Bhate, S.S. (2021). Potential Use-Cases of Natural Language Processing for a Logistics Organization. In: Gunjan, V.K., Zurada, J.M. (eds) Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough. Studies in Computational Intelligence, vol 956. Springer, Cham. https://doi.org/10.1007/978-3-030-68291-0_13

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