@inproceedings{pranesh-shekhar-2020-analysis,
    title = "Analysis of Resource-efficient Predictive Models for Natural Language Processing",
    author = "Pranesh, Raj  and
      Shekhar, Ambesh",
    editor = "Moosavi, Nafise Sadat  and
      Fan, Angela  and
      Shwartz, Vered  and
      Glava{\v{s}}, Goran  and
      Joty, Shafiq  and
      Wang, Alex  and
      Wolf, Thomas",
    booktitle = "Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2020.sustainlp-1.18/",
    doi = "10.18653/v1/2020.sustainlp-1.18",
    pages = "136--140",
    abstract = "In this paper, we presented an analyses of the resource efficient predictive models, namely Bonsai, Binary Neighbor Compression(BNC), ProtoNN, Random Forest, Naive Bayes and Support vector machine(SVM), in the machine learning field for resource constraint devices. These models try to minimize resource requirements like RAM and storage without hurting the accuracy much. We utilized these models on multiple benchmark natural language processing tasks, which were sentimental analysis, spam message detection, emotion analysis and fake news classification. The experiment results shows that the tree-based algorithm, Bonsai, surpassed the rest of the machine learning algorithms by achieve higher accuracy scores while having significantly lower memory usage."
}