| 书目名称 | Machine Learning for Text | | 编辑 | Charu C. Aggarwal | | 视频video | | | 概述 | Integrates treatment of text mining/learning, information retrieval and natural language processing.Has a strong focus on deep learning, transformers and pre-trained language models.Simplifies the mat | | 图书封面 |  | | 描述 | This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis..2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. .3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge | | 出版日期 | Textbook 2022Latest edition | | 关键词 | Machine Learning; Deep Learning; Neural Networks; Transformers; Text Mining; Natural Language Processing; | | 版次 | 2 | | doi | https://doi.org/10.1007/978-3-030-96623-2 | | isbn_softcover | 978-3-030-96625-6 | | isbn_ebook | 978-3-030-96623-2 | | copyright | Springer Nature Switzerland AG 2022 |
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发表于 2025-3-21 17:21:49
