This article shows how you can do Part-of-Speech Tagging of words in your text document in Natural Language Toolkit (NLTK).
Part-of-Speech Tagging means classifying word tokens into their respective part-of-speech and labeling them with the part-of-speech tag.
The tagging is done based on the definition of the word and its context in the sentence or phrase.
Tagging Example:
(‘film’, ‘NN’) => The word ‘film’ is tagged with a noun part of speech tag (‘NN’).
Simple Example (Tagging Single Sentence)
Here’s a simple example of Part-of-Speech (POS) Tagging. We take a simple one sentence text and tag all the words of the sentence using NLTK’s pos_tag module.
from nltk import pos_tag
from nltk.tokenize import word_tokenize
text = "They received the best film of the year award."
text_tokens = word_tokenize(text)
print (text_tokens)
'''
Output:
['They', 'received', 'the', 'best', 'film', 'of', 'the', 'year', 'award', '.']
'''
text_tagged = pos_tag(text_tokens)
print (text_tagged)
'''
Output:
[('They', 'PRP'), ('received', 'VBD'), ('the', 'DT'), ('best', 'JJS'), ('film', 'NN'), ('of', 'IN'), ('the', 'DT'), ('year', 'NN'), ('award', 'NN'), ('.', '.')]
'''
Tagging Multiple Sentences
In this example, we have a text with 4 sentences. Note the word best in all 4 sentences. The same word “best” is used differently in all of the four sentences.
The word best is used as different part-of-speech in each sentence.
– First sentence = Verb (The goal was to best the competition.)
– Second sentence = Noun (His latest song was a personal best.)
– Third sentence = Adjective (Hence, he received the best song of the year award.)
– Fourth sentence = Adverb (He played best after a couple of martinis.)
pos_tag_sents module is used to POS Tag on sentence level.
from nltk import pos_tag_sents
from nltk.tokenize import word_tokenize, sent_tokenize
text = "The goal was to best the competition. His latest song was a personal best. Hence, he received the best song of the year award. He played best after a couple of martinis."
text_sentence_tokens = sent_tokenize(text)
print (text_tokens)
'''
Output:
['The', 'goal', 'was', 'to', 'best', 'the', 'competition', '.', 'His', 'latest', 'song', 'was', 'a', 'personal', 'best', '.', 'Hence', ',', 'he', 'received', 'the', 'best', 'song', 'of', 'the', 'year', 'award', '.', 'He', 'played', 'best', 'after', 'a', 'couple', 'of', 'martinis', '.']
'''
text_word_tokens = []
for sentence_token in text_sentence_tokens:
text_word_tokens.append(word_tokenize(sentence_token))
print (text_word_tokens)
'''
Output:
[['The', 'goal', 'was', 'to', 'best', 'the', 'competition', '.'], ['His', 'latest', 'song', 'was', 'a', 'personal', 'best', '.'], ['Hence', ',', 'he', 'received', 'the', 'best', 'song', 'of', 'the', 'year', 'award', '.'], ['He', 'played', 'best', 'after', 'a', 'couple', 'of', 'martinis', '.']]
'''
text_tagged = pos_tag_sents(text_word_tokens)
print (text_tagged)
'''
Output:
[[('The', 'DT'), ('goal', 'NN'), ('was', 'VBD'), ('to', 'TO'), ('best', 'VB'), ('the', 'DT'), ('competition', 'NN'), ('.', '.')], [('His', 'PRP$'), ('latest', 'JJS'), ('song', 'NN'), ('was', 'VBD'), ('a', 'DT'), ('personal', 'JJ'), ('best', 'NN'), ('.', '.')], [('Hence', 'RB'), (',', ','), ('he', 'PRP'), ('received', 'VBD'), ('the', 'DT'), ('best', 'JJS'), ('song', 'NN'), ('of', 'IN'), ('the', 'DT'), ('year', 'NN'), ('award', 'NN'), ('.', '.')], [('He', 'PRP'), ('played', 'VBD'), ('best', 'RB'), ('after', 'IN'), ('a', 'DT'), ('couple', 'NN'), ('of', 'IN'), ('martinis', 'NN'), ('.', '.')]]
'''
Getting Information of Tags used in NLTK POS Tagger
The following code will show all the tags used in NLTK POS Tagger.
Get information about any particular tag
import nltk
print (nltk.help.upenn_tagset('RB'))
'''
Output:
RB: adverb
occasionally unabatingly maddeningly adventurously professedly
stirringly prominently technologically magisterially predominately
swiftly fiscally pitilessly ...
'''
Get information of Tags starting from any particular letter (using Regular Expression)
import nltk
print (nltk.help.upenn_tagset('PR.*'))
'''
Output:
PRP: pronoun, personal
hers herself him himself hisself it itself me myself one oneself ours
ourselves ownself self she thee theirs them themselves they thou thy us
PRP$: pronoun, possessive
her his mine my our ours their thy your
'''
import nltk
#print (nltk.help.upenn_tagset('P.*'))
'''
Output:
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
'''
Get information about all tags used in NLTK POS Tagger
import nltk
#print (nltk.help.upenn_tagset())
'''
Output:
$: 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
'''
Hope this helps. Thanks.