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Author Goncalves, Bruno, author

Title Natural Language Processing / Goncalves, Bruno
Edition First edition
Published Addison-Wesley Professional, 2018
Online access available from:
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Description 1 online resource (streaming video file) (2 hours, 21 minutes, 0 seconds)
Summary 2+ Hours of Video Instruction Overview Natural Language Processing LiveLessons covers the fundamentals of natural language processing (NLP). It introduces you to the basic concepts, ideas, and algorithms necessary to develop your own NLP applications in a step-by-step and intuitive fashion. The lessons follow a gradual progression, from the more specific to the more abstract, taking you from the very basics to some of the most recent and sophisticated algorithms. About the Instructor Bruno Goncalves is a senior data scientist in the area of complex systems, human behavior, and finance. He has been programming in Python since 2005. For the past five years, his work has focused on NLP and computational linguistics applications on social media. Find out more about Bruno and his work at www.bgoncalves.com . Skill Level Intermediate Learn How To Represent text Model topics Conduct sentiment analysis Understand word2vec word embeddings Define GloVe Apply language detection Who Should Take This Course Data scientists with an interest in natural language processing Course Requirements Basic algebra Calculus and statistics Programming experience Lesson Descriptions Lesson 1: Text Representations The first step in any NLP application is to establish the representations of text and numbers. One-hot encodings provide us with a sparse approach to representing words and n-grams, while bag-of-words improves memory efficiency even further. Naturally, not all words are meaningful, so the next steps are to remove meaningless stop words and to identify the most relevant words for our application using term frequency/inverse document frequency (TF/IDF). Finally, the lesson covers how to identify the stems of words so you can meaningfully reduce the size of your vocabulary. Lesson 2: Topic Modeling Lesson 2 builds on the text representations of Lesson 1 to develop ways of identifying the main subject or subjects of a text. Bruno starts by defining topics and how they can be identified. Next, you learn how to perform explicit semantic analysis to find documents mentioning a specific topic and how to cluster documents according to topics. Latent semantic analysis provides yet another powerful way to extract meaning from raw text, while non-negative matrix factorization enables you to identify latent dimensions in the text, perform recommendations, and measure similarities. Lesson 3: Sentiment Analysis After covering how to represent text in a meaningful way and .
Notes Mode of access: World Wide Web
Pearson Education 2019
Issuing Body Made available through: Safari, an O'Reilly Media Company
Subject Engineering.
Semantic Web.
Linked data.
Electronic data processing -- Structured techniques.
Form Streaming video
Author Safari, an O'Reilly Media Company