Python Text Processing with NLTK 2.0 Cookbook
The learn-by-doing approach of this book will enable you to dive right into the heart of text processing from the very first page. Each recipe is carefully designed to fulfill your appetite for Natural Language Processing. Packed with numerous illustrative examples and code samples, it will make the task of using the NLTK for Natural Language Processing easy and straightforward. This book is for Python programmers who want to quickly get to grips with using the NLTK for Natural Language Processing. Familiarity with basic text processing concepts is required. Programmers experienced in the NLTK will also find it useful. Students of linguistics will find it invaluable.
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algorithm antonyms backoff tagging BeautifulSoup bigrams binary classifiers block reader categories=None channel character encodings chunk chunker ChunkString ClassifierBasedTagger Converting corpora corpus reader corpus view create datetime dateutil DecisionTreeClassifier def __init__(self default DefaultTagger doit encoding execnet Extracting feature sets fileids fileids=None FreqDist function gateways Getting ready hash map high information words Howitworks hypernym install IOB tags itworks keyword argument kwargs label LazyCorpusLoader lemmas lxml MaxentClassifier method module movie_reviews naive Bayes classifier NaiveBayesClassifier named entity natural language processing Ngram Ngram taggers NLTK nltk.corpus import nodes noun parsing partofspeech tags patterns phrase pickle previous recipe Python recipe in Chapter Redis RedisHashFreqDist RegexpParser regular expressions replacement scipy score SequentialBackoffTagger stopwords subclass subtrees synonyms synsets tagged sentence tagged word TaggedCorpusReader test_feats Text Classification There's todoit tokenized sentence Tokenizing Text tree Tree('NP Tree('S treebank corpus tuples unigram UnigramTagger verb WordNet YAML