000			02587nam a22002297a 4500
999			_c3392 _d3392
005			20220906153412.0
008			220906b ||||| |||| 00| 0 eng d
020			_a9781484252741
082			_a006.35 _bSAR
100			_aSarkar, Dipanjan _98258
245			_aText analytics with python: a practitioner's guide to natural language processing
250			_a2nd
260			_bApress _aNew York _c2019
300			_a674 p.
365			_aINR _b1999.00
520			_aAbout this book Leverage 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.
650			_aPython (Computer program language) _98481
650			_aNatural language processing (Computer science) _97016
650			_aData mining _9365
650			_aBig data _9212
942			_2ddc _cBK