Natural Language Processing with Python Cookbook by Dangeti, Pratap -- Read -- Imperial Library of Trantor

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Natural Language Processing with Python Cookbook Title Page Copyright Natural Language Processing with Python Cookbook Credits About the Authors About the Reviewer www.PacktPub.com Why subscribe? Customer Feedback Table of Contents Preface What this book covers What you need for this book Who this book is for Sections Getting ready How to do it… How it works… There's more… See also Conventions Reader feedback Customer support Downloading the example code Errata Piracy Questions Corpus and WordNet Introduction Accessing in-built corpora How to do it... Download an external corpus, load it, and access it Getting ready How to do it... How it works... Counting all the wh words in three different genres in the Brown corpus Getting ready How to do it... How it works... Explore frequency distribution operations on one of the web and chat text corpus files Getting ready How to do it... How it works... Take an ambiguous word and explore all its senses using WordNet Getting ready How to do it... How it works... Pick two distinct synsets and explore the concepts of hyponyms and hypernyms using WordNet Getting ready How to do it... How it works... Compute the average polysemy of nouns, verbs, adjectives, and adverbs according to WordNet Getting ready How to do it... How it works... Raw Text, Sourcing, and Normalization Introduction The importance of string operations Getting ready… How to do it… How it works… Getting deeper with string operations How to do it… How it works… Reading a PDF file in Python Getting ready How to do it… How it works… Reading Word documents in Python Getting ready… How to do it… How it works… Taking PDF, DOCX, and plain text files and creating a user-defined corpus from them Getting ready How to do it… How it works… Read contents from an RSS feed Getting ready How to do it… How it works… HTML parsing using BeautifulSoup Getting ready How to do it… How it works… Pre-Processing Introduction Tokenization – learning to use the inbuilt tokenizers of NLTK Getting ready How to do it… How it works… Stemming – learning to use the inbuilt stemmers of NLTK Getting ready How to do it… How it works… Lemmatization – learning to use the WordnetLemmatizer of NLTK Getting ready How to do it… How it works… Stopwords – learning to use the stopwords corpus and seeing the difference it can make Getting ready How to do it... How it works... Edit distance – writing your own algorithm to find edit distance between two strings Getting ready How to do it… How it works… Processing two short stories and extracting the common vocabulary between two of them Getting ready How to do it… How it works… Regular Expressions Introduction Regular expression – learning to use *, +, and ? Getting ready How to do it… How it works… Regular expression – learning to use $ and ^, and the non-start and non-end of a word Getting ready How to do it… How it works… Searching multiple literal strings and substring occurrences Getting ready How to do it… How it works... Learning to create date regex and a set of characters or ranges of character How to do it... How it works... Find all five-character words and make abbreviations in some sentences How to do it… How it works... Learning to write your own regex tokenizer Getting ready How to do it... How it works... Learning to write your own regex stemmer Getting ready How to do it… How it works… POS Tagging and Grammars Introduction Exploring the in-built tagger Getting ready How to do it... How it works... Writing your own tagger Getting ready How to do it... How it works... Training your own tagger Getting ready How to do it... How it works... Learning to write your own grammar Getting ready How to do it... How it works... Writing a probabilistic CFG Getting ready How to do it... How it works... Writing a recursive CFG Getting ready How to do it... How it works... Chunking, Sentence Parse, and Dependencies Introduction Using the built-in chunker Getting ready How to do it... How it works... Writing your own simple chunker Getting ready How to do it... How it works... Training a chunker Getting ready How to do it... How it works... Parsing recursive descent Getting ready How to do it... How it works... Parsing shift-reduce Getting ready How to do it... How it works... Parsing dependency grammar and projective dependency Getting ready How to do it... How it works... Parsing a chart Getting ready How to do it... How it works... Information Extraction and Text Classification Introduction Understanding named entities Using inbuilt NERs Getting ready How to do it... How it works... Creating, inversing, and using dictionaries Getting ready How to do it... How it works... Choosing the feature set Getting ready How to do it... How it works... Segmenting sentences using classification Getting ready How to do it... How it works... Classifying documents Getting ready How to do it... How it works... Writing a POS tagger with context Getting ready How to do it... How it works... Advanced NLP Recipes Introduction Creating an NLP pipeline Getting ready How to do it... How it works... Solving the text similarity problem Getting ready How to do it... How it works... Identifying topics Getting ready How to do it... How it works... Summarizing text Getting ready How to do it... How it works... Resolving anaphora Getting ready How to do it... How it works... Disambiguating word sense Getting ready How to do it... How it works... Performing sentiment analysis Getting ready How to do it... How it works... Exploring advanced sentiment analysis Getting ready How to do it... How it works... Creating a conversational assistant or chatbot Getting ready How to do it... How it works... Applications of Deep Learning in NLP Introduction Convolutional neural networks Applications of CNNs Recurrent neural networks Application of RNNs in NLP Classification of emails using deep neural networks after generating TF-IDF Getting ready How to do it... How it works... IMDB sentiment classification using convolutional networks CNN 1D Getting ready How to do it... How it works... IMDB sentiment classification using bidirectional LSTM Getting ready How to do it... How it works... Visualization of high-dimensional words in 2D with neural word vector visualization Getting ready How to do it... How it works... Advanced Applications of Deep Learning in NLP Introduction Automated text generation from Shakespeare's writings using LSTM Getting ready... How to do it... How it works... Questions and answers on episodic data using memory networks Getting ready... How to do it... How it works... Language modeling to predict the next best word using recurrent neural networks LSTM Getting ready... How to do it... How it works... Generative chatbot using recurrent neural networks (LSTM) Getting ready... How to do it... How it works...

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