使用Java和LingPipe进行自然语言处理实战

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"Natural Language Processing with Java and LingPipe Cookbook 是一本专为有经验的Java开发者设计的实战指南,旨在帮助他们快速有效地提升自然语言处理(NLP)技能。书中涵盖了从基础到高级的NLP技术,包括语言识别、情感分类器、评估框架,以及使用LingPipe工具包构建复杂NLP系统的方法。读者需要具备基本的NLP术语知识。书中的章节包括简单的分类器、词汇处理、高级分类器、词性标注、文本分块、字符串比较与聚类和核心ference概念/人物的查找。" 在这本书中,作者首先介绍了基础但强大的技术,如语言识别,这涉及到确定文本的语言,这对于多语种环境下的应用至关重要。情感分类器则用于分析文本中的情绪倾向,常见于社交媒体分析和市场研究。同时,书中还讲解了评估框架,这是确保模型性能的关键。 接下来,书中深入讨论了如何构建一个强大的NLP框架,以解决诸如词性标注、词和标记的识别等常见问题。词性标注是将单词与其在句子中的语法角色关联起来的过程,对于理解文本结构和含义至关重要。文本分块(Chunking)则是识别连续的文本片段,如短语或从句,有助于更精细地分析文本内容。 此外,本书还涉及到了字符串比较和聚类,这是对文本进行相似性分析和归类的基础。这些技术可以应用于文档分类、推荐系统或新闻聚合等领域。核心ference识别则涉及找出文本中提到的概念或人名是否指的是同一个实体,这对于理解和解析复杂文本至关重要。 在高级技术部分,书中介绍了逻辑回归、条件随机场和潜在狄利克雷分配等机器学习方法,这些都是构建复杂NLP系统的常用工具。这些技术能够处理更复杂的任务,如命名实体识别、关系抽取和语义角色标注等。 "Natural Language Processing with Java and LingPipe Cookbook" 是一本适合希望在Java环境中应用NLP技术的开发者的实用指南。通过书中详尽的实例和逐步指导,读者可以学习如何利用LingPipe有效地实现各种NLP任务,从而提升他们的应用开发能力。
2019-07-04 上传
Book Description Natural Language Processing (NLP) has become one of the prime technologies for processing very large amounts of unstructured data from disparate information sources. This book includes a wide set of recipes and quick methods that solve challenges in text syntax, semantics, and speech tasks. At the beginning of the book, you'll learn important NLP techniques, such as identifying parts of speech, tagging words, and analyzing word semantics. You will learn how to perform lexical analysis and use machine learning techniques to speed up NLP operations. With independent recipes, you will explore techniques for customizing your existing NLP engines/models using Java libraries such as OpenNLP and the Stanford NLP library. You will also learn how to use NLP processing features from cloud-based sources, including Google and Amazon's AWS. You will master core tasks, such as stemming, lemmatization, part-of-speech tagging, and named entity recognition. You will also learn about sentiment analysis, semantic text similarity, language identification, machine translation, and text summarization. By the end of this book, you will be ready to become a professional NLP expert using a problem-solution approach to analyze any sort of text, sentences, or semantic words. What you will learn Explore how to use tokenizers in NLP processing Implement NLP techniques in machine learning and deep learning applications Identify sentences within the text and learn how to train specialized NER models Learn how to classify documents and perform sentiment analysis Find semantic similarities between text elements and extract text from a variety of sources Preprocess text from a variety of data sources Learn how to identify and translate languages