Natural Language Processing with Spark NLP teaches text processing within the Apache Spark ecosystem. Alex Thomas connects linguistic foundations with tokenization, tagging, entity recognition, sentiment analysis, classification, and search through Scala and Python examples. The book addresses data scientists and engineers who know Spark and want to build distributed text-processing pipelines.
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Alex Thomas begins with the representation and processing of language before developing practical pipelines with Spark NLP. Tokenization, lemmatization, part-of-speech tagging, and named entity recognition provide the building blocks. Scala and Python examples connect the concepts with the distributed processing environment of Apache Spark.
Later topics include sentiment analysis, text classification, search, embeddings, and the use of pretrained models. The progression considers how individual language-processing tasks contribute to applications that handle large collections of text. Data scientists, software engineers, and machine learning practitioners familiar with Spark can use this 2020 first edition to study the relationship between NLP concepts, pipeline design, and distributed deployment.
Text processing at scale changes what an NLP pipeline must do. This book pairs the linguistic building blocks of tokenization, tagging and entity recognition with the distributed machinery of Apache Spark, using Scala and Python examples to show how the pieces become applications that handle large text collections.
Thomas starts with how language is represented and processed before moving to the practical pipeline. Tokenization, lemmatization, part-of-speech tagging and named entity recognition are introduced as the components on which later tasks depend. Scala and Python examples tie each step to Spark, the environment that makes the processing distributed. Sentiment analysis, text classification, search, embeddings and pretrained models extend the sequence toward full applications. The through-line is the relationship between a single language-processing task, the design of a pipeline, and deployment across large volumes of text. The 2020 first edition is aimed at readers who already know Spark.
The book begins with the question of how text is turned into something a system can operate on. Representation choices affect every later step, so the opening material sets constraints rather than offering neutral background. This foundation makes the pipeline stages intelligible as a sequence of transformations.
Tokenization splits text into units and lemmatization reduces words to a base form. Both are presented as preparatory steps whose decisions ripple through downstream tasks. The text treats them as adjustable components rather than fixed preprocessing.
Tagging assigns grammatical categories to tokens and supplies information that later tasks can use. The book connects tags to the syntactic structure they imply. It presents tagging as a component with its own accuracy concerns and its own place in the pipeline.
Entity recognition identifies and classifies names and other referents in text. It demonstrates how linguistic annotation becomes structured information that applications can act on. The topic also shows why entity boundaries and categories are not always straightforward.
Sentiment analysis and text classification turn processed text into judgments or categories. The book treats these as applications built on the earlier components, not as isolated tricks. Scala and Python examples connect the model or method to the distributed execution environment.
Search, word embeddings and pretrained models extend the pipeline toward retrieval and reuse. Pretrained models reduce the need to begin from scratch while introducing their own integration questions. The closing concern is deployment: how a pipeline built from these pieces runs across large text collections.
Data scientists, software engineers and machine learning practitioners who already know Spark and want to build distributed text-processing pipelines.
Edition, audience, and study-guide details at a glance.
Natural Language Processing with Spark NLP teaches text processing within the Apache Spark ecosystem. Alex Thomas connects linguistic foundations with tokenization, tagging, entity recognition, sentiment analysis, classification, and search through Scala and Python examples. The book addresses data scientists and engineers who know Spark and want to build distributed text-processing pipelines.
Data scientists, software engineers and machine learning practitioners who already know Spark and want to build distributed text-processing pipelines.
Yes. This page includes an original study guide with six thematic sections, key takeaways, and a topic map for structured reading.
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