基于混合深度学习方法的越南语关键词提取

Bui Thanh Hung
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引用次数: 6

摘要

关键词提供了一种简短的方式来反映文件的主要思想,使读者更容易阅读。关键词提取是自然语言处理中的主要任务。由于手动提取关键字不仅耗时,而且需要付出大量努力,因此需要使用自动化方法。本文提出了一种基于混合深度学习的越南语关键词自动提取方法。每一种现有的深度学习方法都有自己的优势;我们引入的混合深度学习模型是CNN和LSTM模型的优势特征的结合。与另一种方法相比,所提出的模型显示出更高的准确性和f1分数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vietnamese Keyword Extraction Using Hybrid Deep Learning Methods
Keywords provide a short way of reflecting a main idea of the document, making it easier for the readers in reading. Extracting keyword is the main task in natural language processing. Since it is not only time consuming but also requires lots of efforts to extract the keywords manually, it arises the need for the automated approaches. This paper has proposed a solution for the automatic keyword extraction in Vietnamese language using hybrid deep learning approaches. Every existing deep learning approach has its own advantages; and the hybrid deep learning model we are introducing is the combination of the superior features of CNN and LSTM models. The proposed model shows enhanced accuracy and f1-score over another approach.
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