Python 3 text processing with NLTK 3 cookbook : over 80 practical recipes on natural language processing techniques using Python's NLTK 3.0 / Jacob Perkins
Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Tokenizing Text and WordNet Basics; Introduction; Tokenizing text into sentences; Tokenizing sentences into words; Tokenizing sentences using regular expressions; Training a sentence tokenizer; Filtering stopwords in a tokenized sentence; Looking up Synsets for a word in WordNet; Looking up lemmas and synonyms in WordNet; Calculating WordNet Synset similarity; Discovering word collocations; Chapter 2: Replacing and Correcting Words; Introduction; Stemming words
Lemmatizing words with WordNetReplacing words matching regular expressions; Removing repeating characters; Spelling correction with Enchant; Replacing synonyms; Replacing negations with antonyms; Chapter 3: Creating Custom Corpora; Introduction; Setting up a custom corpus; Creating a wordlist corpus; Creating a part-of-speech tagged word corpus; Creating a chunked phrase corpus; Creating a categorized text corpus; Creating a categorized chunk corpus reader; Lazy corpus loading; Creating a custom corpus view; Creating a MongoDB-backed corpus reader; Corpus editing with file locking
Chapter 4: Part-of-speech TaggingIntroduction; Default tagging; Training a unigram part-of-speech tagger; Combining taggers with backoff tagging; Training and combining ngram taggers; Creating a model of likely word tags; Tagging with regular expressions; Affix tagging; Training a Brill tagger; Training the TnT tagger; Using WordNet for tagging; Tagging proper names; Classifier-based tagging; Training a tagger with NLTK-Trainer; Chapter 5: Extracting Chunks; Introduction; Chunking and chinking with regular expressions; Merging and splitting chunks with regular expressions
Expanding and removing chunks with regular expressionsPartial parsing with regular expressions; Training a tagger-based chunker; Classification-based chunking; Extracting named entities; Extracting proper noun chunks; Extracting location chunks; Training a named entity chunker; Training a chunker with NLTK-Trainer; Chapter 6: Transforming Chunks and Trees; Introduction; Filtering insignificant words from a sentence; Correcting verb forms; Swapping verb phrases; Swapping noun cardinals; Swapping infinitive phrases; Singularizing plural nouns; Chaining chunk transformations
Converting a chunk tree to textFlattening a deep tree; Creating a shallow tree; Converting tree labels; Chapter 7: Text Classification; Introduction; Bag of words feature extraction; Training a Naive Bayes classifier; Training a decision tree classifier; Training a maximum entropy classifier; Training scikit-learn classifiers; Measuring precision and recall of a classifier; Calculating high information words; Combining classifiers with voting; Classifying with multiple binary classifiers; Training a classifier with NLTK-Trainer; Chapter 8: Distributed Processing and Handling Large Datasets
Notes
Online resource; title from cover (Safari, viewed Sept. 19, 2014)
