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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learning-path-java-natural-language-processing-with-java/
课程评论:没有评论
课程名称:学习路径:Java:自然语言处理 课程概述:自然语言处理(NLP)在许多应用中被广泛使用,提供了之前无法实现的能力。NLP涉及分析文本以获取意图和含义,这些信息可用于支持应用程序。将NLP应用于项目需要结合标准的Java技术和通常基于经过训练模型的专用库。如果您想学习强大的Java自然语言处理技术,那么这个学习路径非常适合您。 本学习路径的亮点包括: - 根据特定的文本处理需求进行分词 - 提取文本元素之间的关系 在学习路径的开始,您将逐步了解基本的NLP任务,包括数据获取、数据清理、文本部分查找和句子结束的判断。这些基础为后续的NLP任务奠定了基础,如文本分类和文本元素之间关系的确定。随后,您将学习分词技术,分词几乎是所有NLP任务的基础。您会学到如何拆分文本,以揭示信息,例如人名、日期,甚至句子的语法结构。这类活动可以揭示文本元素之间的关系及文档中嵌入的意义。接下来,您将基于数据规范化、分词和句子边界检测(SBD)等基本NLP任务,执行更专业的NLP任务。您将不仅仅是寻找文本中的单词,还能够识别出具体元素,如人名或地点。最后,您将学习如何将句子拆分成基本的语法单位,这又是一项从文本中提取意义和关系的任务。在学习路径的末尾,您将准备好应对更高级的NLP任务,运用Java中的自然语言处理技术。 专家介绍:我们结合了以下尊敬作者的最佳作品,确保您的学习之旅顺利: - Kamesh Balasubramanian:Wirecog, LLC的创始人兼首席执行官,拥有20多年软件开发经验,曾在多个行业实施解决方案。 - Ben Tranter:拥有近六年经验的开发者,专注于数据挖掘及其他领域应用的构建。 - Rostislav Dzinko:软件架构师,超过六年行业经验,是Go语言的早期用户,成功应用于生产中。 本课程通过一系列逻辑性和循序渐进的视频产品,帮助您按部就班地提升在自然语言处理方面的技能。
Natural Language Processing is used in many applications to provide capabilities that were previously not possible. It involves analyzing text to obtain the intent and meaning, which can then be used to support an application. Using NLP within an application requires a combination of standard Java techniques and often specialized libraries frequently based on models that have been trained. If you're interested to learn the powerful Natural Language Processing techniques with Java, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. The highlights of this Learning Path are: Perform tokenization based on specific text processing needs Extract the relationship between elements of text This Learning Path covers the essence of NLP using Java. This Learning Path will commence by walking you through basic NLP tasks including data acquisition, data cleaning, finding parts of text, and determining the end of sentences. These serve as the basis for other NLP tasks such as classifying text and determining the relationship between text elements. This will be followed by the use of tokenization techniques. Tokenization is used for almost all NLP tasks. You'll learn how text can be split to reveal information such as names, dates, and even the grammatical structure of a sentence. These types of activity can lead to insights into the relationships between text elements and embedded meaning in a document. You'll then start by building on the basic NLP tasks of data normalization, tokenization, and SBD to perform more specialized NLP tasks. You'll be able to do more than simply find a word in the text. You'll also identify specific elements such as a person's name or a location from the text. Finally, you'll learn to split a sentence into basic grammatical units is another task that enables you to extract meaning and relationships from text. Towards the end of this Learning Path, you will be ready to take on more advanced NLP tasks with Natural Language Processing techniques using Java. Meet Your Experts: We have combined the best works of the following esteemed authors to ensure that your learning journey is smooth: Kamesh Balasubramanian is the founder and CEO of Wirecog, LLC. He is the inventor of Wireframe Cognition (Wirecog), an award-winning, patented technology that allows machines to understand wireframe designs and produce source code from them. Kamesh has over 20 years' software development experience and has implemented numerous solutions in the advertising, entertainment, media, publishing, hospitality, videogame, legal, and government sectors. He is an award-winning, professional member of the Association for Computing Machinery and an InfyMaker Award winner. He was recognized as a Maker of Change at the 2016 World Maker Faire in New York and, upon request, has demonstrated Wirecog at MIT. Ben Tranter is a developer with nearly six years' experience. He has worked with a variety of companies to build applications in Go, in the areas of data mining, web back ends, user authentication services, and developer tools, and is a contributor to a variety of open source Go projects. Rostislav Dzinko is a software architect who has been working in the software development industry for more than six years. He was one of the first developers who started working with the Go language far earlier than the first official public release of Go 1.0 took place. Rostislav uses the Go language daily and has successfully used it in production for more than two years, building a broad range of software from high-load web applications to command-line utilities. He has a Master's degree in Systems Engineering and has completed a PhD thesis.