AI for AI:什么NLP技术帮助研究人员找到关于NLP的正确文章

Sergei P. Prokhorov, Victor Safronov
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引用次数: 7

摘要

未来几年人类的进步很大程度上与人工智能方法领域的成功有关。这一领域的知识增长本质上是信息爆炸。研究人员不得不花费太多时间监控一个不断发展的领域,过滤掉复杂的文档和文本流,这些文档和文本需要深入了解机器学习方法及其在特定领域的应用。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关系的能力使现代语言模型能够有效地解决信息检索、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,这些方法构成了现代文本信息检索系统和半自动编写评论和路线图的基础。文献语义分类的文本向量化方法综述。还讨论了这些技术的已知局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI for AI: What NLP Techniques Help Researchers Find the Right Articles on NLP
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.
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