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AI for AI: What NLP Techniques Help Researchers Find the Right Articles on NLP

机译:AI为AI:NLP技术帮助研究人员找到了关于NLP的合适文章

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The human progress of the coming years is largely associated with success in the field of artificial intelligence methods. The growth of knowledge in this area is in the nature of an information explosion. Researchers have to spend too much time monitoring a constantly evolving field, filtering out the flow of complex documents and texts that require a deep understanding of machine learning methods and their application in specific areas. In solving this problem, the very methods of machine learning and natural language processing are highly effective. The ability to take into account complex non-linear dependencies allows modern language models to effectively solve the problems of information retrieval, monitoring, facts extraction and further analysis. The article provides an overview of modern natural language processing methods that form the basis of modern text information retrieval systems and semi-automatic compilation of reviews and roadmaps. A review of approaches of text vectorization methods for semantic classification of documents. Known limitations of these techniques are also discussed.
机译:未来几年的人类进步在很大程度上与人工智能方法领域的成功相关。该地区知识的增长是信息爆炸的性质。研究人员必须花费太多时间监控不断发展的字段,过滤掉其需要深入了解机器学习方法及其在特定领域的应用程序的复杂文档和文本的流程。在解决这个问题时,机器学习和自然语言处理的方法非常有效。考虑复杂的非线性依赖性的能力允许现代语言模型有效解决信息检索,监测,事实提取和进一步分析的问题。本文概述了现代自然语言处理方法,形成了现代文本信息检索系统的基础,以及评论和路线图的半自动汇编。文本语义分类文本矢量化方法方法述评。还讨论了这些技术的已知局限性。

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