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019 889674234|a907279783|a961486506
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020 1782167854
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050 4 QA76.73.P98|b.P47 2014eb
072 7 COM|x051360|2bisacsh
082 04 005.133|223
090 QA76.73.P98|b.P47 2014eb
100 1 Perkins, Jacob,|0https://id.loc.gov/authorities/names/
n2004074455|eauthor.
245 10 Python 3 text processing with NLTK 3 cookbook :|bover 80
practical recipes on natural language processing
techniques using Python's NLTK 3.0 /|cJacob Perkins ;
cover image by Faiz Fattohi.
250 Second edition.
264 1 Birmingham, England :|bPackt Publishing Ltd,|c2014.
264 4 |c©2014
300 1 online resource (304 pages) :|billustrations
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
340 |gpolychrome|2rdacc
347 text file|2rdaft
385 |nage|aChildren|2lcdgt
500 "Quick answers to common problems"--Cover.
500 Includes index.
505 0 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.
505 8 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.
505 8 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.
505 8 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.
505 8 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.
520 This book is intended for Python programmers interested in
learning how to do natural language processing. Maybe
you've learned the limits of regular expressions the hard
way, or you've realized that human language cannot be
deterministically parsed like a computer language. Perhaps
you have more text than you know what to do with, and need
automated ways to analyze and structure that text. This
Cookbook will show you how to train and use statistical
language models to process text in ways that are
practically impossible with standard programming tools. A
basic knowledge of Python and the basi.
588 0 Online resource; title from PDF title page (ebrary, viewed
September 2, 2014).
590 eBooks on EBSCOhost|bEBSCO eBook Subscription Academic
Collection - North America
650 0 Python (Computer program language)|0https://id.loc.gov/
authorities/subjects/sh96008834|vJuvenile literature.
|0https://id.loc.gov/authorities/subjects/sh99001674
650 0 Natural language processing (Computer science)|0https://
id.loc.gov/authorities/subjects/sh88002425|xResearch.
|0https://id.loc.gov/authorities/subjects/sh2002006576
650 7 Python (Computer program language)|2fast|0https://
id.worldcat.org/fast/1084736
650 7 Natural language processing (Computer science)|2fast
|0https://id.worldcat.org/fast/1034365
650 7 Research.|2fast|0https://id.worldcat.org/fast/1095153
655 0 Electronic books.
655 4 Electronic books.
700 1 Fattohi, Faiz,|ecover designer.
776 08 |iPrint version:|aPerkins, Jacob.|tPython 3 text
processing with NLTK 3 cookbook : over 80 practical
recipes on natural language processing techniques using
Python's NLTK 3.0.|bSecond edition.|dBirmingham, England :
Packt Publishing Ltd, ©2014|hiii, 288 pages|z9781782167853
856 40 |uhttps://rider.idm.oclc.org/login?url=http://
search.ebscohost.com/login.aspx?direct=true&scope=site&
db=nlebk&AN=836632|zOnline eBook. Access restricted to
current Rider University students, faculty, and staff.
856 42 |3Instructions for reading/downloading this eBook|uhttp://
guides.rider.edu/ebooks/ebsco
901 MARCIVE 20231220
948 |d20170505|cEBSCO|tebscoebooksacademic new|lridw
994 92|bRID
