Evaluating the noise tolerance of Cloud NLP services across Amazon, Microsoft, and Google

IF 8.2 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers in Industry Pub Date : 2024-11-14 DOI:10.1016/j.compind.2024.104211

Juliano Barbosa , Baldoino Fonseca , Márcio Ribeiro , João Correia , Leandro Dias da Silva , Rohit Gheyi , Davy Baia

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引用次数: 0

Abstract

Natural Language Processing (NLP) has revolutionized industries, streamlining customer service through applications in healthcare, finance, legal, and human resources domains, and simplifying tasks like medical research, financial analysis, and sentiment analysis. To avoid the high costs of building and maintaining NLP infrastructure, companies turn to Cloud NLP services offered by major cloud providers like Amazon, Google, and Microsoft. However, there is little knowledge about how tolerant these services are when subjected to noise. This paper presents a study that analyzes the effectiveness of Cloud NLP services by evaluating the noise tolerance of sentiment analysis services provided by Amazon, Google, and Microsoft when subjected to 12 types of noise, including syntactic and semantic noises. The findings indicate that Google is the most tolerant to syntactic noises, and Microsoft is the most tolerant to semantic noises. These findings may help developers and companies in selecting the most suitable service provider and shed light towards improving state-of-the-art techniques for effective cloud NLP services.

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评估亚马逊、微软和谷歌云 NLP 服务的噪音容忍度

自然语言处理（NLP）为各行各业带来了变革，通过在医疗保健、金融、法律和人力资源领域的应用，简化了客户服务，并简化了医学研究、金融分析和情感分析等任务。为了避免构建和维护 NLP 基础设施的高昂成本，企业转向亚马逊、谷歌和微软等主要云计算提供商提供的云 NLP 服务。然而，人们对这些服务在受到噪声影响时的容忍度知之甚少。本文介绍了一项研究，通过评估亚马逊、谷歌和微软提供的情感分析服务在受到 12 种噪音（包括句法和语义噪音）影响时的噪音容忍度，分析了云 NLP 服务的有效性。研究结果表明，谷歌对句法噪声的容忍度最高，而微软对语义噪声的容忍度最高。这些发现可以帮助开发人员和公司选择最合适的服务提供商，并有助于改进最先进的技术，从而提供有效的云计算 NLP 服务。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers in Industry 工程技术-计算机：跨学科应用

CiteScore

18.90

自引率

8.00%

发文量

152

审稿时长

22 days

期刊介绍： The objective of Computers in Industry is to present original, high-quality, application-oriented research papers that: • Illuminate emerging trends and possibilities in the utilization of Information and Communication Technology in industry; • Establish connections or integrations across various technology domains within the expansive realm of computer applications for industry; • Foster connections or integrations across diverse application areas of ICT in industry.