印度尼西亚地方语言的语言识别算法分析

Herry Sujaini, Arif Bijaksana Putra
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引用次数: 0

摘要

检测印度尼西亚的地方语言对于认识语言多样性、促进文化间理解、保护濒危语言以及改善教育和服务的获取至关重要。通过识别和记录这些语言,我们可以支持语言保护工作,为社区提供量身定制的资源,并弘扬不同民族的独特文化遗产。最终,这将鼓励建立一个更加包容和开放的社会,优先考虑各种语言和文化习俗。本研究旨在确定最适合印尼地区语言检测的算法,并通过 n-gram 分析深入了解这些语言的独特性。通过了解语言多样性,本研究有助于保护印尼的文化和语言遗产,并改进语言检测技术。本研究比较了五种算法(奈夫贝叶斯、K-近邻(KNN)、最小二乘、库尔贝克莱布勒发散和 Kolmogorov Smirnov 检验)的性能,以确定最准确、最有效的语言识别方法。将三字格特征与单字格和双字格特征结合在一起可显著提高模型的性能,F1 分数从 0.923 提高到 0.959。研究发现,使用更多的特征可以提高准确性,奈夫贝叶斯和 KNN 是表现最好的语言识别算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of language identification algorithms for regional Indonesian languages
Detecting local languages in Indonesia is essential for recognizing linguistic diversity, promoting intercultural understanding, preserving endangered languages, and improving access to education and services. By identifying and documenting these languages, we can support language preservation efforts, provide tailored resources for communities, and celebrate the unique cultural heritage of different ethnic groups. Ultimately, this encourages a more accepting and open-minded society, prioritizing various languages and cultural customs. This research aims to identify the most suitable algorithm for language detection in Indonesian regional languages and gain insights into their unique characteristics through n-gram analysis. By understanding language diversity, the study contributes to preserving Indonesia's cultural and linguistic heritage and improving language detection techniques. This study compares the performance of five algorithms (Naïve Bayes, K-nearest neighbors (KNN), least-squares, Kullback Leibler divergence, and Kolmogorov Smirnov test) to determine the most accurate and efficient method for language identification. Incorporating trigram features alongside unigrams and bigrams significantly improved the model's performance, with F1 scores increasing from 0.923 to 0.959. The study found that using more features leads to better accuracy, with Naïve Bayes and KNN emerging as the top-performing algorithms for language identification.
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