| Description |
1 online resource |
| Summary |
Elevate your natural language processing skills with Gensim and become proficient in handling a wide range of NLP tasks and projects Key Features Advance your NLP skills with this comprehensive guide covering detailed explanations and code practices Build real-world topical modeling pipelines and fine-tune hyperparameters to deliver optimal results Adhere to the real-world industrial applications of topic modeling in medical, legal, and other fields Purchase of the print or Kindle book includes a free PDF eBook Book Description Navigating the terrain of NLP research and applying it practically can be a formidable task made easy with The Handbook of NLP with Gensim. This book demystifies NLP and equips you with hands-on strategies spanning healthcare, e-commerce, finance, and more to enable you to leverage Gensim in real-world scenarios. You'll begin by exploring motives and techniques for extracting text information like bag-of-words, TF-IDF, and word embeddings. This book will then guide you on topic modeling using methods such as Latent Semantic Analysis (LSA) for dimensionality reduction and discovering latent semantic relationships in text data, Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, and Ensemble LDA to enhance topic modeling stability and accuracy. Next, you'll learn text summarization techniques with Word2Vec and Doc2Vec to build the modeling pipeline and optimize models using hyperparameters. As you get acquainted with practical applications in various industries, this book will inspire you to design innovative projects. Alongside topic modeling, you'll also explore named entity handling and NER tools, modeling procedures, and tools for effective topic modeling applications. By the end of this book, you'll have mastered the techniques essential to create applications with Gensim and integrate NLP into your business processes. What you will learn Convert text into numerical values such as bag-of-word, TF-IDF, and word embedding Use various NLP techniques with Gensim, including Word2Vec, Doc2Vec, LSA, FastText, LDA, and Ensemble LDA Build topical modeling pipelines and visualize the results of topic models Implement text summarization for legal, clinical, or other documents Apply core NLP techniques in healthcare, finance, and e-commerce Create efficient chatbots by harnessing Gensim's NLP capabilities Who this book is for This book is for data scientists and professionals who want to become proficient in topic modeling with Gensim. NLP practitioners can use this book as a code reference, while students or those considering a career transition will find this a valuable resource for advancing in the field of NLP. This book contains real-world applications for biomedical, healthcare, legal, and operations, making it a helpful guide for project managers designing their own topic modeling applications. |
| Contents |
Cover -- Title Page -- Copyright and Credits -- Contributors -- Table of Contents -- Preface -- Part 1: NLP Basics -- Chapter 1: Introduction to NLP -- Introduction to natural language processing -- NLU + NLG = NLP -- NLU -- NLG -- Gensim and its NLP modeling techniques -- BoW and TF-IDF -- LSA/LSI -- Word2Vec -- Doc2Vec -- LDA -- Ensemble LDA -- Topic modeling with BERTopic -- Common NLP Python modules included in this book -- spaCy -- NLTK -- Summary -- Questions -- References -- Chapter 2: Text Representation -- Technical requirements -- What word embedding is -- Simple encoding methods |
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One-hot encoding -- BoW -- Bag-of-N-grams -- What TF-IDF is -- Shining applications of BoW and TF-IDF -- Coding -- BoW -- Gensim for BoW -- scikit-learn for BoW (CountVectorizer) -- Coding -- Bag-of-N-grams -- Gensim for N-grams -- scikit-learn for N-grams -- NLTK for N-grams -- Coding -- TF-IDF -- Gensim for TF-IDF -- scikit-learn for TF-IDF -- Summary -- Questions -- References -- Chapter 3: Text Wrangling and Preprocessing -- Technical requirements -- Key steps in NLP preprocessing -- Tokenization -- Lowercase conversion -- Stop word removal -- Punctuation removal -- Stemming -- Lemmatization |
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Coding with spaCy -- spaCy for lemmatization -- spaCy for PoS -- Coding with NLTK -- NLTK for tokenization -- NLTK for stop-word removal -- NLTK for lemmatization -- Coding with Gensim -- Gensim for preprocessing -- Gensim for stop-word removal -- Gensim for stemming -- Building a pipeline with spaCy -- Summary -- Questions -- References -- Part 2: Latent Semantic Analysis/Latent Semantic Indexing -- Chapter 4: Latent Semantic Analysis with scikit-learn -- Technical requirements -- Understanding matrix operations -- An orthogonal matrix -- The determinant of a matrix |
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Understanding a transformation matrix -- A transformation matrix in daily life examples -- Understanding eigenvectors and eigenvalues -- An introduction to SVD -- Truncated SVD -- Truncated SVD for LSI -- Coding truncatedSVD with scikit-learn -- Using TruncatedSVD -- randomized_SVD -- Using TruncatedSVD for LSI with real data -- Loading the data -- Creating TF-IDF -- Using TruncatedSVD to build a model -- Interpreting the outcome -- Summary -- Questions -- Chapter 5: Cosine Similarity -- Technical requirements -- What is cosine similarity? -- How cosine similarity is used in images |
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How to compute cosine similarity with scikit-learn -- Summary -- Questions -- References -- Chapter 6: Latent Semantic Indexing with Gensim -- Technical requirements -- Performing text preprocessing -- Performing word embedding with BoW and TF-IDF -- BoW -- TF-IDF -- Modeling with Gensim -- BoW -- TF-IDF -- Using the coherence score to find the optimal number of topics -- Saving the model for production -- Using the model as an information retrieval tool -- Loading the dictionary list -- Preprocessing the new document -- Scoring the document to get the latent topic scores |
| Subject |
Natural language processing (Computer science) -- Software.
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Python (Computer program language)
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Open source software.
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Traitement automatique des langues naturelles -- Logiciels. |
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Python (Langage de programmation) |
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Logiciels libres. |
| Other Form: |
Print version: 1803244941 9781803244945 (OCoLC)1401654662 |
| ISBN |
9781803245508 (electronic bk.) |
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1803245506 (electronic bk.) |
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