Personalization of News for a Logistics Organisation by Finding Relevancy Using NLP

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


Artificial Intelligence-driven applications have already stepped in to streamline logistics on a global scale. News impact and relevancy helps in taking the right decision at the right time in the logistics industry. This paper attempts to provide a state of art in finding relevancy in news headlines. We present the research done on logistics data using natural language processing. In this paper, we will explain the different algorithms we have used as well as the various embedding strategies we have tried to find news relevancy. We have used statistical and deep learning models to extract information from the corpora. The proposed methods are compared based on relevancy score and results are significantly acceptable.


Logistics Artificial intelligence Natural language processing (NLP) Deep learning News relevancy Word embedding 



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.

Conflicts of Interest

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.


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© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Dr. Babasaheb Ambedkar Technological UniversityLonereIndia
  2. 2.ATA Freight Line India Pvt. Ltd.PuneIndia

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