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Ray, Sukanya: Building Domain Ontologies and Au...
49,00 € *
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Erscheinungsdatum: 08/2012, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Building Domain Ontologies and Automated Text Categorization, Titelzusatz: a contribution to NLP, Autor: Ray, Sukanya // Chandra, Nidhi, Verlag: LAP Lambert Academic Publishing, Sprache: Englisch, Rubrik: Informatik // EDV, Sonstiges, Seiten: 68, Informationen: Paperback, Gewicht: 118 gr, Verkäufer: averdo

Anbieter: averdo
Stand: 04.08.2020
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Hands-On Python Natural Language Processing
57,99 € *
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Erscheinungsdatum: 26.06.2020, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Hands-On Python Natural Language Processing, Titelzusatz: Explore tools and techniques to analyze and process text with a view to building real-world NLP applications, Autor: Kedia, Aman // Rasu, Mayank, Verlag: Packt Publishing, Sprache: Englisch, Rubrik: Informatik // EDV, Sonstiges, Seiten: 316, Informationen: Paperback, Gewicht: 575 gr, Verkäufer: averdo

Anbieter: averdo
Stand: 04.08.2020
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Text, Speech and Dialogue
115,39 € *
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Erscheinungsdatum: 11.09.2006, Medium: Taschenbuch, Einband: Kartoniert / Broschiert, Titel: Text, Speech and Dialogue, Titelzusatz: 9th International Conference, TSD 2006, Brno, Czech Republic, September 11-15, 2006, Proceedings, Auflage: 2006, Redaktion: Kopecek, Ivan // Pala, Karel // Sojka, Petr, Verlag: Springer Berlin Heidelberg // Springer Berlin, Sprache: Englisch, Schlagworte: Kognitive Linguistik // Sprachwissenschaft // NLP // Neurolinguistische Programmierung // Recherche // Information Retrieval // Spracherkennung, Rubrik: Informatik, Seiten: 744, Informationen: Paperback, Gewicht: 1107 gr, Verkäufer: averdo

Anbieter: averdo
Stand: 04.08.2020
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NLP-Driven Document Representations for Text Ca...
48,99 € *
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NLP-Driven Document Representations for Text Categorization ab 48.99 € als Taschenbuch: Empirical Selection of NLP-Driven Document Representations for Text Categorization. Aus dem Bereich: Bücher, English, International, Gebundene Ausgaben,

Anbieter: hugendubel
Stand: 04.08.2020
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Text Analytics with Python, Second Edition
24,46 € *
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Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You'll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques. There is also a chapter dedicated to semantic analysis where you'll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release. What You'll Learn - Understand NLP and text syntax, semantics and structure - Discover text cleaning and feature engineering - Review text classification and text clustering - Assess text summarization and topic models - Study deep learning for NLP Who This Book Is For IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.

Anbieter: buecher
Stand: 04.08.2020
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Text Analytics with Python, Second Edition
24,46 € *
ggf. zzgl. Versand

Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. This second edition has gone through a major revamp and introduces several significant changes and new topics based on the recent trends in NLP. You'll see how to use the latest state-of-the-art frameworks in NLP, coupled with machine learning and deep learning models for supervised sentiment analysis powered by Python to solve actual case studies. Start by reviewing Python for NLP fundamentals on strings and text data and move on to engineering representation methods for text data, including both traditional statistical models and newer deep learning-based embedding models. Improved techniques and new methods around parsing and processing text are discussed as well. Text summarization and topic models have been overhauled so the book showcases how to build, tune, and interpret topic models in the context of an interest dataset on NIPS conference papers. Additionally, the book covers text similarity techniques with a real-world example of movie recommenders, along with sentiment analysis using supervised and unsupervised techniques. There is also a chapter dedicated to semantic analysis where you'll see how to build your own named entity recognition (NER) system from scratch. While the overall structure of the book remains the same, the entire code base, modules, and chapters has been updated to the latest Python 3.x release. What You'll Learn - Understand NLP and text syntax, semantics and structure - Discover text cleaning and feature engineering - Review text classification and text clustering - Assess text summarization and topic models - Study deep learning for NLP Who This Book Is For IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.

Anbieter: buecher
Stand: 04.08.2020
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Natural Language Processing in Action: Understa...
9,95 € *
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Natural Language Processing in Action is your guide to building machines that can read and interpret human language. In it, you'll use readily available Python packages to capture the meaning in text and react accordingly. The book expands traditional NLP approaches to include neural networks, modern deep learning algorithms, and generative techniques as you tackle real-world problems like extracting dates and names, composing text, and answering free-form questions.About the TechnologyRecent advances in deep learning empower applications to understand text and speech with extreme accuracy. The result? Chatbots that can imitate real people, meaningful resume-to-job matches, superb predictive search, and automatically generated document summaries - all at a low cost. New techniques, along with accessible tools like Keras and TensorFlow, make professional-quality NLP easier than ever before.What's inside:Some sentences in this book were written by NLP! Can you guess which ones?Working with Keras, TensorFlow, gensim, and scikit-learn.Rule-based and data-based NLP.Scalable pipelines.RequirementsThis book requires a basic understanding of deep learning and intermediate Python skills.Hobson Lane, Cole Howard, and Hannes Max Hapke are experienced NLP engineers who use these techniques in production for profit and fun: contributing to social-benefit projects like smart guides for people with blindness and cognitive assistance for those with developmental challenges or suffering from information overload (don't we all?)."Provides a great overview of current NLP tools in Python. I’ll definitely be keeping this book on hand for my own NLP work. Highly recommended!" (Tony Mullen, Northeastern University - Seattle)"An intuitive guide to get you started with NLP. The book is full of programming examples that help you learn in a v 1. Language: English. Narrator: Mark Thomas. Audio sample: http://samples.audible.de/bk/acx0/162830/bk_acx0_162830_sample.mp3. Digital audiobook in aax.

Anbieter: Audible
Stand: 04.08.2020
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Deep Learning for NLP and Speech Recognition
79,02 € *
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This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.

Anbieter: buecher
Stand: 04.08.2020
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Deep Learning for NLP and Speech Recognition
79,02 € *
ggf. zzgl. Versand

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.

Anbieter: buecher
Stand: 04.08.2020
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