LEADER 04916nam 22006135 450 001 9910338005403321 005 20211125105609.0 010 $a9781484243541 010 $a1484243544 024 7 $a10.1007/978-1-4842-4354-1 035 $a(CKB)4100000008280627 035 $a(MiAaPQ)EBC5778375 035 $a(DE-He213)978-1-4842-4354-1 035 $a(CaSebORM)9781484243541 035 $a(PPN)236525921 035 $a(OCoLC)1107052646 035 $a(OCoLC)on1107052646 035 $a(EXLCZ)994100000008280627 100 $a20190521d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aText Analytics with Python $eA Practitioner's Guide to Natural Language Processing /$fby Dipanjan Sarkar 205 $a2nd ed. 2019. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2019. 215 $a1 online resource (688 pages) 311 08$a9781484243534 311 08$a1484243536 320 $aIncludes bibliographical references. 327 $aChapter 1: Natural Language Processing Basics -- Chapter 2: Python for Natural Language Processing -- Chapter 3: Processing and Understanding Text -- Chapter 4: Feature Engineering for Text Data -- Chapter 5: Text Classification -- Chapter 6: Text summarization and topic modeling -- Chapter 7: Text Clustering and Similarity analysis -- Chapter 8: Sentiment Analysis -- Chapter 9: Deep learning in NLP. 330 $aLeverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. This edition has gone through a major revamp introducing several major changes and new topics based on the recent trends in NLP. We have a dedicated chapter around Python for NLP covering fundamentals on how to work with strings and text data along with introducing the current state-of-the-art open-source frameworks in NLP. We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models. Techniques around parsing and processing text data have also been improved with some new methods. Considering popular NLP applications, for text classification, we also cover methods for tuning and improving our models. Text Summarization has gone through a major overhaul in the context of topic models where we showcase how to build, tune and interpret topic models in the context of an interest dataset on NIPS conference papers. Similarly, we cover text similarity techniques with a real-world example of movie recommenders. Sentiment Analysis is covered in-depth with both supervised and unsupervised techniques. We also cover both machine learning and deep learning models for supervised sentiment analysis. Semantic Analysis gets its own dedicated chapter where we also showcase how you can build your own Named Entity Recognition (NER) system from scratch. To conclude things, we also have a completely new chapter on the promised of Deep Learning for NLP where we also showcase a hands-on example on deep transfer learning. While the overall structure of the book remains the same, the entire code base, modules, and chapters will be updated to the latest Python 3.x release. ---------------------------------- Also the key selling points ? Implementations are based on Python 3.x and state-of-the-art popular open source libraries in NLP ? Covers Machine Learning and Deep Learning for Advanced Text Analytics and NLP ? Showcases diverse NLP applications including Classification, Clustering, Similarity Recommenders, Topic Models, Sentiment and Semantic Analysis. 517 3 $aPractitioner's guide to natural language processing 606 $aArtificial intelligence 606 $aPython (Computer program language) 606 $aBig data 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aPython$3https://scigraph.springernature.com/ontologies/product-market-codes/I29080 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 615 0$aArtificial intelligence. 615 0$aPython (Computer program language) 615 0$aBig data. 615 14$aArtificial Intelligence. 615 24$aPython. 615 24$aBig Data. 676 $a005.133 700 $aSarkar$b Dipanjan$4aut$4http://id.loc.gov/vocabulary/relators/aut$0785722 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910338005403321 996 $aText analytics with Python$91749316 997 $aUNINA