python -m pip install nltk==3.5
python -m pip install numpy matplotlib
python
import nltk
nltk.download()

Tokenizing
Tokenizing by word:
Tokenizing by sentence:

from nltk.tokenize import sent_tokenize, word_tokenize
example_string = """
... Muad'Dib learned rapidly because his first training was in how to learn.
... And the first lesson of all was the basic trust that he could learn.
... It's shocking to find how many people do not believe they can learn,
... and how many more believe learning to be difficult."""

You can use sent_tokenize() to split up example_string into sentences:

>>> sent_tokenize(example_string)
["Muad'Dib learned rapidly because his first training was in how to learn.",
'And the first lesson of all was the basic trust that he could learn.',
"It's shocking to find how many people do not believe they can learn, and how many more believe learning to be difficult."]

Tokenizing example_string by sentence gives you a list of three strings that are sentences:

"Muad'Dib learned rapidly because his first training was in how to learn."
'And the first lesson of all was the basic trust that he could learn.'
"It's shocking to find how many people do not believe they can learn, and how many more believe learning to be difficult."

Now try tokenizing example_string by word:

>>> word_tokenize(example_string)
["Muad'Dib",
'learned',
'rapidly',
'because',
'his',
'first',
'training',
'was',
'in',
'how',
'to',
'learn',
'.',
'And',
'the',
'first',
'lesson',
'of',
'all',
'was',
'the',
'basic',
'trust',
'that',
'he',
'could',
'learn',
'.',
'It',
"'s",
'shocking',
'to',
'find',
'how',
'many',
'people',
'do',
'not',
'believe',
'they',
'can',
'learn',
',',
'and',
'how',
'many',
'more',
'believe',
'learning',
'to',
'be',
'difficult',
'.']

You got a list of strings that NLTK considers to be words, such as:

"Muad'Dib"
'training'
'how'
But the following strings were also considered to be words:

"'s"
','
'.'

See how "It's" was split at the apostrophe to give you 'It' and "'s", but "Muad'Dib" was left whole? This happened because NLTK knows that 'It' and "'s" (a contraction of “is”) are two distinct words, so it counted them separately. But "Muad'Dib" isn’t an accepted contraction like "It's", so it wasn’t read as two separate words and was left intact.

Filtering Stop Words

Stop words are words that you want to ignore, so you filter them out of your text when you’re processing it. Very common words like 'in', 'is', and 'an' are often used as stop words since they don’t add a lot of meaning to a text in and of themselves.

Here’s how to import the relevant parts of NLTK in order to filter out stop words:
>>> nltk.download("stopwords")
>>> from nltk.corpus import stopwords
>>> from nltk.tokenize import word_tokenize
stop_words = set(stopwords.words("english"))
>>> stop_words
{'each', "needn't", "doesn't", 'he', 'all', "wouldn't", 'has', 'him', "don't", 'herself', 'after', 'nor', 'here', 'further', 'hers', 'how', 'some', 'as', 'up', 'd', 'ma', 'this', 'their', 'so', 'during', 'my', "it's", 'its', 'and', 'ourselves', "you'll", 'haven', 'whom', 'at', 'itself', 'be', 'from', 'just', 'until', 'been', 'theirs', "aren't", 'why', 'yours', 'when', 'no', 'it', 'had', 'do', 'same', 'such', 's', 'most', 'into', "mightn't", 'your', 'y', 'that', 'doesn', 't', 'about', "isn't", 'won', 'doing', 'was', 'have', 'than', 'very', 'can', 'didn', 'those', 'me', 'or', 'once', 'm', 're', 'ours', 'again', 'any', 'aren', 'what', 'were', 'a', 'for', 'off', 'them', "haven't", 'isn', 'o', 'more', 'our', 'she', 'couldn', 'yourselves', 'in', "didn't", "mustn't", "couldn't", 'then', 'only', "you're", "won't", 'having', 'ain', "weren't", 'where', 'which', 'before', "shan't", 'hadn', 'am', "should've", 'is', 'mightn', 'below', 'her', 'myself', 'on', "that'll", 'mustn', 'i', 'does', 'don', "you'd", 'but', 'both', 'by', 'who', 'an', 'there', 'shan', 'are', "hadn't", "she's", "you've", 'being', 'between', 'hasn', 'to', 'you', 'his', 'down', 'own', 'did', 'out', 'should', 'shouldn', 'other', 'against', 'themselves', 'if', 'the', 'will', 'wasn', 'above', 'not', 'now', 'because', 'll', 'we', 'these', 'weren', 'they', 'few', 'yourself', 'under', 'himself', 'over', 'needn', 'through', "hasn't", 'while', 'of', 'too', 'with', 'wouldn', "wasn't", "shouldn't", 've'}

>>> worf_quote = "Sir, I protest. I am not a merry man!"

>>> words_in_quote = word_tokenize(worf_quote)
>>> words_in_quote
['Sir', ',', 'I', 'protest', '.', 'I', 'am', 'not', 'a', 'merry', 'man', '!']

>>> filtered_list = [word for word in words_in_quote if word.casefold() not in stop_words]
>>> filtered_list
['Sir', ',', 'protest', '.', 'merry', 'man', '!']

Stemming #词干提取
Stemming is a text processing task in which you reduce words to their root, which is the core part of a word. For example, the words “helping” and “helper” share the root “help.” Stemming allows you to zero in on the basic meaning of a word rather than all the details of how it’s being used. NLTK has more than one stemmer, but you’ll be using the Porter stemmer.

Here’s how to import the relevant parts of NLTK in order to start stemming:

>>> from nltk.stem import PorterStemmer
>>> from nltk.tokenize import word_tokenize
>>> stemmer = PorterStemmer()
>>> string_for_stemming = """
... The crew of the USS Discovery discovered many discoveries.
... Discovering is what explorers do."""
>>> words = word_tokenize(string_for_stemming)
>>> words
['The', 'crew', 'of', 'the', 'USS', 'Discovery', 'discovered', 'many', 'discoveries', '.', 'Discovering', 'is', 'what', 'explorers', 'do', '.']

>>> stemmed_words = [stemmer.stem(word) for word in words]
>>> stemmed_words
['the', 'crew', 'of', 'the', 'uss', 'discoveri', 'discov', 'mani', 'discoveri', '.', 'discov', 'is', 'what', 'explor', 'do', '.']

Tagging Parts of Speech #标记词性
Part of speech is a grammatical term that deals with the roles words play when you use them together in sentences. Tagging parts of speech, or POS tagging, is the task of labeling the words in your text according to their part of speech.
sagan_quote = """
If you wish to make an apple pie from scratch,
you must first invent the universe."""

>>> from nltk.tokenize import word_tokenize
>>> sagan_quote = """
... If you wish to make an apple pie from scratch,
... you must first invent the universe."""
>>> sagan_quote
'\nIf you wish to make an apple pie from scratch,\nyou must first invent the universe.'
>>> words_in_sagan_quote = word_tokenize(sagan_quote)
>>> words_in_sagan_quote
['If', 'you', 'wish', 'to', 'make', 'an', 'apple', 'pie', 'from', 'scratch', ',', 'you', 'must', 'first', 'invent', 'the', 'universe', '.']
>>> import nltk
>>> nltk.pos_tag(words_in_sagan_quote)
[('If', 'IN'), ('you', 'PRP'), ('wish', 'VBP'), ('to', 'TO'), ('make', 'VB'), ('an', 'DT'), ('apple', 'NN'), ('pie', 'NN'), ('from', 'IN'), ('scratch', 'NN'), (',', ','), ('you', 'PRP'), ('must', 'MD'), ('first', 'VB'), ('invent', 'VB'), ('the', 'DT'), ('universe', 'NN'), ('.', '.')]
>>> nltk.help.upenn_tagset()
$: dollar
$ -$ --$ A$ C$ HK$ M$ NZ$ S$ U.S.$ US$
'': closing quotation mark
' ''
(: opening parenthesis
( [ {
): closing parenthesis
) ] }
,: comma
,
--: dash
--
.: sentence terminator
. ! ?
:: colon or ellipsis
: ; ...
CC: conjunction, coordinating
& 'n and both but either et for less minus neither nor or plus so
therefore times v. versus vs. whether yet
CD: numeral, cardinal
mid-1890 nine-thirty forty-two one-tenth ten million 0.5 one forty-
seven 1987 twenty '79 zero two 78-degrees eighty-four IX '60s .025
fifteen 271,124 dozen quintillion DM2,000 ...
DT: determiner
all an another any both del each either every half la many much nary
neither no some such that the them these this those
EX: existential there
there
FW: foreign word
gemeinschaft hund ich jeux habeas Haementeria Herr K'ang-si vous
lutihaw alai je jour objets salutaris fille quibusdam pas trop Monte
terram fiche oui corporis ...
IN: preposition or conjunction, subordinating
astride among uppon whether out inside pro despite on by throughout
below within for towards near behind atop around if like until below
next into if beside ...
JJ: adjective or numeral, ordinal
third ill-mannered pre-war regrettable oiled calamitous first separable
ectoplasmic battery-powered participatory fourth still-to-be-named
multilingual multi-disciplinary ...
JJR: adjective, comparative
bleaker braver breezier briefer brighter brisker broader bumper busier
calmer cheaper choosier cleaner clearer closer colder commoner costlier
cozier creamier crunchier cuter ...
JJS: adjective, superlative
calmest cheapest choicest classiest cleanest clearest closest commonest
corniest costliest crassest creepiest crudest cutest darkest deadliest
dearest deepest densest dinkiest ...
LS: list item marker
A A. B B. C C. D E F First G H I J K One SP-44001 SP-44002 SP-44005
SP-44007 Second Third Three Two * a b c d first five four one six three
two
MD: modal auxiliary
can cannot could couldn't dare may might must need ought shall should
shouldn't will would
NN: noun, common, singular or mass
common-carrier cabbage knuckle-duster Casino afghan shed thermostat
investment slide humour falloff slick wind hyena override subhumanity
machinist ...
NNP: noun, proper, singular
Motown Venneboerger Czestochwa Ranzer Conchita Trumplane Christos
Oceanside Escobar Kreisler Sawyer Cougar Yvette Ervin ODI Darryl CTCA
Shannon A.K.C. Meltex Liverpool ...
NNPS: noun, proper, plural
Americans Americas Amharas Amityvilles Amusements Anarcho-Syndicalists
Andalusians Andes Andruses Angels Animals Anthony Antilles Antiques
Apache Apaches Apocrypha ...
NNS: noun, common, plural
undergraduates scotches bric-a-brac products bodyguards facets coasts
divestitures storehouses designs clubs fragrances averages
subjectivists apprehensions muses factory-jobs ...
PDT: pre-determiner
all both half many quite such sure this
POS: genitive marker
' 's
PRP: pronoun, personal
hers herself him himself hisself it itself me myself one oneself ours
ourselves ownself self she thee theirs them themselves they thou thy us
PRP$: pronoun, possessive
her his mine my our ours their thy your
RB: adverb
occasionally unabatingly maddeningly adventurously professedly
stirringly prominently technologically magisterially predominately
swiftly fiscally pitilessly ...
RBR: adverb, comparative
further gloomier grander graver greater grimmer harder harsher
healthier heavier higher however larger later leaner lengthier less-
perfectly lesser lonelier longer louder lower more ...
RBS: adverb, superlative
best biggest bluntest earliest farthest first furthest hardest
heartiest highest largest least less most nearest second tightest worst
RP: particle
aboard about across along apart around aside at away back before behind
by crop down ever fast for forth from go high i.e. in into just later
low more off on open out over per pie raising start teeth that through
under unto up up-pp upon whole with you
SYM: symbol
% & ' '' ''. ) ). * + ,. < = > @ A[fj] U.S U.S.S.R * ** ***
TO: "to" as preposition or infinitive marker
to
UH: interjection
Goodbye Goody Gosh Wow Jeepers Jee-sus Hubba Hey Kee-reist Oops amen
huh howdy uh dammit whammo shucks heck anyways whodunnit honey golly
man baby diddle hush sonuvabitch ...
VB: verb, base form
ask assemble assess assign assume atone attention avoid bake balkanize
bank begin behold believe bend benefit bevel beware bless boil bomb
boost brace break bring broil brush build ...
VBD: verb, past tense
dipped pleaded swiped regummed soaked tidied convened halted registered
cushioned exacted snubbed strode aimed adopted belied figgered
speculated wore appreciated contemplated ...
VBG: verb, present participle or gerund
telegraphing stirring focusing angering judging stalling lactating
hankerin' alleging veering capping approaching traveling besieging
encrypting interrupting erasing wincing ...
VBN: verb, past participle
multihulled dilapidated aerosolized chaired languished panelized used
experimented flourished imitated reunifed factored condensed sheared
unsettled primed dubbed desired ...
VBP: verb, present tense, not 3rd person singular
predominate wrap resort sue twist spill cure lengthen brush terminate
appear tend stray glisten obtain comprise detest tease attract
emphasize mold postpone sever return wag ...
VBZ: verb, present tense, 3rd person singular
bases reconstructs marks mixes displeases seals carps weaves snatches
slumps stretches authorizes smolders pictures emerges stockpiles
seduces fizzes uses bolsters slaps speaks pleads ...
WDT: WH-determiner
that what whatever which whichever
WP: WH-pronoun
that what whatever whatsoever which who whom whosoever
WP$: WH-pronoun, possessive
whose
WRB: Wh-adverb
how however whence whenever where whereby whereever wherein whereof why
``: opening quotation mark

Lemmatizing #词形还原
Now that you’re up to speed on parts of speech, you can circle back to lemmatizing. Like stemming, lemmatizing reduces words to their core meaning, but it will give you a complete English word that makes sense on its own instead of just a fragment of a word like 'discoveri'.
>>> from nltk.stem import WordNetLemmatizer
>>> lemmatizer = WordNetLemmatizer()
>>> lemmatizer.lemmatize("scarves")
'scarf'
>>> string_for_lemmatizing = "The friends of DeSoto love scarves."
>>> words = word_tokenize(string_for_lemmatizing)
>>> words
['The', 'friends', 'of', 'DeSoto', 'love', 'scarves', '.']
>>> lemmatized_words = [lemmatizer.lemmatize(word) for word in words]
>>> lemmatized_words
['The', 'friend', 'of', 'DeSoto', 'love', 'scarf', '.']
>>> lemmatizer.lemmatize("worst")
'worst'
>>> lemmatizer.lemmatize("worst", pos="a")
'bad'

Chunking #分块 ，短语
While tokenizing allows you to identify words and sentences, chunking allows you to identify phrases.
Chunking makes use of POS tags to group words and apply chunk tags to those groups. Chunks don’t overlap, so one instance of a word can be in only one chunk at a time.

Here’s how to import the relevant parts of NLTK in order to chunk:

 

Now tokenize that string by word:

>>> from nltk.tokenize import word_tokenize
>>> lotr_quote = "It's a dangerous business, Frodo, going out your door."
>>> words_in_lotr_quote = word_tokenize(lotr_quote)
>>> words_in_lotr_quote
['It', "'s", 'a', 'dangerous', 'business', ',', 'Frodo', ',', 'going', 'out', 'your', 'door', '.']
Now you’ve got a list of all of the words in lotr_quote.
>>> nltk.download("averaged_perceptron_tagger")
[nltk_data] Downloading package averaged_perceptron_tagger to
[nltk_data] C:\Users\songyuejie\AppData\Roaming\nltk_data...
[nltk_data] Package averaged_perceptron_tagger is already up-to-
[nltk_data] date!
True
The next step is to tag those words by part of speech:
>>> lotr_pos_tags = nltk.pos_tag(words_in_lotr_quote)
>>> lotr_pos_tags
[('It', 'PRP'), ("'s", 'VBZ'), ('a', 'DT'), ('dangerous', 'JJ'), ('business', 'NN'), (',', ','), ('Frodo', 'NNP'), (',', ','), ('going', 'VBG'), ('out', 'RP'), ('your', 'PRP$'), ('door', 'NN'), ('.', '.')]
You’ve got a list of tuples of all the words in the quote, along with their POS tag.
In order to chunk, you first need to define a chunk grammar.
Create a chunk grammar with one regular expression rule:
>>> grammar = "NP: {<DT>?<JJ>*<NN>}"
According to the rule you created, your chunks:

Start with an optional (?) determiner ('DT')
Can have any number (*) of adjectives (JJ)
End with a noun (<NN>)
Create a chunk parser with this grammar:

>>> chunk_parser = nltk.RegexpParser(grammar)
>>> tree = chunk_parser.parse(lotr_pos_tags)
>>> tree.draw()

You got two noun phrases:

'a dangerous business' has a determiner, an adjective, and a noun.
'door' has just a noun.

Chinking
Chinking is used together with chunking, but while chunking is used to include a pattern, chinking is used to exclude a pattern.
>>> from nltk.tokenize import word_tokenize
>>> lotr_quote = "It's a dangerous business, Frodo, going out your door."
>>> words_in_lotr_quote = word_tokenize(lotr_quote)
>>> words_in_lotr_quote
['It', "'s", 'a', 'dangerous', 'business', ',', 'Frodo', ',', 'going', 'out', 'your', 'door', '.']
>>> lotr_pos_tags = nltk.pos_tag(words_in_lotr_quote)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'nltk' is not defined
>>> import nltk
>>> lotr_pos_tags = nltk.pos_tag(words_in_lotr_quote)
>>> lotr_pos_tags
[('It', 'PRP'), ("'s", 'VBZ'), ('a', 'DT'), ('dangerous', 'JJ'), ('business', 'NN'), (',', ','), ('Frodo', 'NNP'), (',', ','), ('going', 'VBG'), ('out', 'RP'), ('your', 'PRP$'), ('door', 'NN'), ('.', '.')]
>>> grammar = """
... Chunk: {<.*>+}
... }<JJ>{"""
>>> chunk_parser = nltk.RegexpParser(grammar)
>>> tree = chunk_parser.parse(lotr_pos_tags)
>>> tree
Tree('S', [Tree('Chunk', [('It', 'PRP'), ("'s", 'VBZ'), ('a', 'DT')]), ('dangerous', 'JJ'), Tree('Chunk', [('business', 'NN'), (',', ','), ('Frodo', 'NNP'), (',', ','), ('going', 'VBG'), ('out', 'RP'), ('your', 'PRP$'), ('door', 'NN'), ('.', '.')])])
>>> tree.draw()