摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-在一份新的报告中讨论了人工智能的研究结果。根据NewsRx记者来自Debre M Arkos大学的新闻，研究表明，“根据自然语言处理专家的说法，语言中有许多模棱两可的词。”我们的新闻记者从Debre Markos大学的研究中获得了一句话：“没有任何语言的自动词义消歧，自然语言处理技术的发展，如信息提取、信息检索、机器翻译等，仍然是一项挑战任务。因此，我们的新闻记者从Debre Markos大学的研究中获得了一句话：“没有任何语言的自动词义消歧，自然语言处理技术的发展，如信息检索本文利用Corpus-Based方法建立了格埃兹语复字母词词义分离模型,由于格埃兹语没有wordNet或公共数据集,收集了1010个歧义词样本,然后利用词袋、词频反向文档频、词然后利用有监督机器学习算法Naive Bayes、决策树、随机森林、k近邻、线性支持向量机和Logistic回归对矢量化文本进行分析，结果表明，随机词组对EST的分类效果优于其他组合，准确率为99.52%。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on artificial intell igence are discussed in a new report. According to news originating from Debre M arkos University by NewsRx correspondents, research stated, “According to natura l language processing experts, there are numerous ambiguous words in languages.” Our news correspondents obtained a quote from the research from Debre Markos Uni versity: “Without automated word meaning disambiguation for any language, the de velopment of natural language processing technologies such as information extrac tion, information retrieval, machine translation, and others are still challengi ng task. Therfore, this paper presents the development of a word sense disambigu ation model for duplicate alphabet words for the Ge’ez language using corpus-bas ed methods. Because there is no wordNet or public dataset for the Ge’ez language , 1010 samples of ambiguous words were gathered. Afterwards, the words were prep rocessed and the text was vectorized using bag of words, Term Frequency-Inverse Document Frequency, and word embeddings such as word2vec and fastText. The vecto rized texts are then analysed using the supervised machine learning algorithms s uch Naive Bayes, decision trees, random forests, K-nearest neighbor, linear supp ort vector machine, and logistic regression. Bag of words paired with random for ests outperformed all other combinations, with an accuracy of 99.52% .”
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