Treffer: Mastering Natural Language Processing with Python

About This BookLearn to implement various NLP tasks in PythonGain insights into the current and budding research topics of NLPThis is a comprehensive step-by-step guide to help students and researchers create their own projects based on real-life applicationsWho This Book Is ForThis book is for intermediate level developers in NLP with a reasonable knowledge level and understanding of Python.What You Will LearnImplement string matching algorithms and normalization techniquesImplement statistical language modeling techniquesGet an insight into developing a stemmer, lemmatizer, morphological analyzer, and morphological generatorDevelop a search engine and implement POS tagging concepts and statistical modeling concepts involving the n-gram approachFamiliarize yourself with concepts such as the Treebank construct, CFG construction, the CYK chart parsing algorithm, and the Earley chart parsing algorithmDevelop a NER-based system and understand and apply the concepts of sentiment analysisUnderstand and implement the concepts of information retrieval and text summarizationDevelop a discourse analysis system and anaphora resolution-based systemIn DetailNatural Language Processing is one of the fields of computational linguistics and artificial intelligence that is concerned with human-computer interaction. It provides seamless interaction between computers and human beings and gives computers the ability to understand human speech with the help of machine learning.This book will show you how to employ various NLP tasks in Python, and give you an insight into the best practices when designing and building NLP-based applications using Python. It will help you become an expert in no time and help you to create your own NLP projects using NLTK.You will sequentially be guided through applying machine learning tools to develop various models. We'll provide clarity regarding the creation of training data and the implementation of major NLP applications such as named entity recognition, question-answering system, discourse analysis, transliteration, word sense disambiguation, information retrieval, sentiment analysis, text summarization, and anaphora resolution.