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

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

Abstract

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.

Keywords

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

Notes

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.

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.

References

  1. 1.
    Liu, X., Chen, C., Liu, S.: Algorithm for ranking news. In: Third International Conference on Semantics, Knowledge and Grid (SKG 2007), pp. 314–317. IEEE (2007)Google Scholar
  2. 2.
    Gao, S., Ma, J., Chen, Z.: Modeling and predicting retweeting dynamics on microblogging platforms. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 107–116 (2015)Google Scholar
  3. 3.
    Agrawal, A., Sahdev, R., Davoudi, H., Khonsari, F., An, A., McGrath, S.: Detecting the magnitude of events from news articles. In: 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 177–184. IEEE (2016)Google Scholar
  4. 4.
    Yang, M., Mahmood, M., Zhou, X., Shafaq, S., Zahid, L.: Design and implementation of cloud platform for intelligent logistics in the trend of intellectualization. China Commun. 14(10), 180–191 (2017)CrossRefGoogle Scholar
  5. 5.
    Bouraoui, A., Jamoussi, S., Hamadou, A.B.: A New method for the construction of evolving embedded representations of words. In: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), Hammamet, pp. 83–87 (2017)Google Scholar
  6. 6.
    Ma, L., Zhang, Y.: Using Word2Vec to process big text data. In: 2015 IEEE International Conference on Big Data (Big Data), pp. 2895–2897. IEEE (2015)Google Scholar
  7. 7.
    Turney, P.D., Pantel, P.: From frequency to meaning: vector space models of semantics. J. Artif. Intell. Res. 37, 141–188 (2010)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Ma, Z., Feng, J., Chen, L., Hu, X., Shi, Y.: An improved approach to terms weighting in text classification. In: 2011 International Conference on Computer and Management (CAMAN), pp. 1–4. IEEE (2011)Google Scholar
  9. 9.
    Yang, Y. Research and realization of internet public opinion analysis based on improved TF-IDF algorithm. In: 2017 16th International Symposium on Distributed Computing and Applications to Business, Engineering and Science (DCABES), pp. 80–83. IEEE (2017)Google Scholar
  10. 10.
    Sun, P., Wang, L., Xia, Q.: The keyword extraction of Chinese medical web page based on WF-TF-IDF algorithm. In: 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), pp. 193–198. IEEE (2017)Google Scholar
  11. 11.
    Roul, R.K., Sahoo, J.K., Arora, K.: Modified TF-IDF term weighting strategies for text categorization. In: 2017 14th IEEE India Council International Conference (INDICON), pp. 1–6. IEEE (2017)Google Scholar
  12. 12.
    Wu, H., Yuan, N.: An improved TF-IDF algorithm based on word frequency distribution information and category distribution information. In: Proceedings of the 3rd International Conference on Intelligent Information Processing, pp. 211–215 (2018)Google Scholar
  13. 13.
    Paik, J.H.: A novel TF-IDF weighting scheme for effective ranking. In: Proceedings of the 36th international ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 343–352 (2013)Google Scholar
  14. 14.
    Jing, L.P., Huang, H.K., Shi, H.B.: Improved feature selection approach TFIDF in text mining. In: Proceedings International Conference on Machine Learning and Cybernetics, vol. 2, pp. 944–946. IEEE (2002)Google Scholar
  15. 15.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188–1196 (2014)Google Scholar
  16. 16.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space. arXiv preprint arXiv:1301.3781 (2013)
  17. 17.
    Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)zbMATHGoogle Scholar
  18. 18.
    Collobert, R., Weston, J.: A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 160–167 (2008)Google Scholar
  19. 19.
    Schwenk, H.: Continuous space language models. Comput. Speech Lang. 21(3), 492–518 (2007)CrossRefGoogle Scholar
  20. 20.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)Google Scholar
  21. 21.
    Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)Google Scholar
  22. 22.
    Shi, T., Liu, Z.: Linking GloVe with word2vec. arXiv preprint arXiv:1411.5595 (2014)
  23. 23.
    Harris, Z.S.: Distributional structure. Word 10(2–3), 146–162 (1954)CrossRefGoogle Scholar

Copyright information

© 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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