Titlebook: Machine Learning for Text; Charu C. Aggarwal Textbook 2022Latest edition Springer Nature Switzerland AG 2022 Machine Learning.Deep Learnin

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ce-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge978-3-030-96625-6978-3-030-96623-2

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nsformers and pre-trained language models.Simplifies the matThis 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 im

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Textbook 2022Latest editionof 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 preproces

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978-3-030-96625-6Springer Nature Switzerland AG 2022

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https://doi.org/10.1007/978-3-030-96623-2Machine Learning; Deep Learning; Neural Networks; Transformers; Text Mining; Natural Language Processing;

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Matrix Factorization and Topic Modeling,Most document collections are defined by document-term matrices in which the rows (or columns) are highly correlated with one another. These correlations can be leveraged to create a low-dimensional representation of the data, and this process is referred to as ..