How do I find a list with all possible pos tags used by the Natural Language Toolkit (nltk)?
The book has a note how to find help on tag sets, e.g.:
Others are probably similar. (Note: Maybe you first have to download
tagsets from the download helper’s Models section for this)
To save some folks some time, here is a list I extracted from a small corpus. I do not know if it is complete, but it should have most (if not all) of the help definitions from upenn_tagset…
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 ...
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
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 ...
NNS: noun, common, plural
undergraduates scotches bric-a-brac products bodyguards facets coasts divestitures storehouses designs clubs fragrances averages subjectivists apprehensions muses factory-jobs ...
all both half many quite such sure this
POS: genitive marker
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
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
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
TO: “to” as preposition or infinitive marker
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 ...
that what whatever which whichever
that what whatever whatsoever which who whom whosoever
how however whence whenever where whereby whereever wherein whereof why
The tag set depends on the corpus that was used to train the tagger.
The default tagger of
nltk.pos_tag() uses the Penn Treebank Tag Set.
In NLTK 2, you could check which tagger is the default tagger as follows:
import nltk nltk.tag._POS_TAGGER 'taggers/maxent_treebank_pos_tagger/english.pickle'
That means that it’s a Maximum Entropy tagger trained on the Treebank corpus.
nltk.tag._POS_TAGGER does not exist anymore in NLTK 3 but the documentation states that the off-the-shelf tagger still uses the Penn Treebank tagset.
The below can be useful to access a dict keyed by abbreviations:
from nltk.data import load tagdict = load('help/tagsets/upenn_tagset.pickle') tagdict['NN'] 'noun, common, singular or mass' tagdict.keys() ['PRP$', 'VBG', 'VBD', '``', 'VBN', ',', "''", 'VBP', 'WDT', ...
The reference is available at the official site
Copy and pasting from there:
- CC | Coordinating conjunction |
- CD | Cardinal number |
- DT | Determiner |
- EX | Existential there |
- FW | Foreign word |
- IN | Preposition or subordinating conjunction |
- JJ | Adjective |
- JJR | Adjective, comparative |
- JJS | Adjective, superlative |
- LS | List item marker |
- MD | Modal |
- NN | Noun, singular or mass |
- NNS | Noun, plural |
- NNP | Proper noun, singular |
- NNPS | Proper noun, plural |
- PDT | Predeterminer |
- POS | Possessive ending |
- PRP | Personal pronoun |
- PRP$ | Possessive pronoun |
- RB | Adverb |
- RBR | Adverb, comparative |
- RBS | Adverb, superlative |
- RP | Particle |
- SYM | Symbol |
- TO | to |
- UH | Interjection |
- VB | Verb, base form |
- VBD | Verb, past tense |
- VBG | Verb, gerund or present participle |
- VBN | Verb, past participle |
- VBP | Verb, non-3rd person singular present |
- VBZ | Verb, 3rd person singular present |
- WDT | Wh-determiner |
- WP | Wh-pronoun |
- WP$ | Possessive wh-pronoun |
- WRB | Wh-adverb |
You can download the list here: ftp://ftp.cis.upenn.edu/pub/treebank/doc/tagguide.ps.gz. It includes confusing parts of speech, capitalization, and other conventions. Also, wikipedia has an interesting section similar to this. Section: Part-of-speech tags used.
['LS', 'TO', 'VBN', "''", 'WP', 'UH', 'VBG', 'JJ', 'VBZ', '--', 'VBP', 'NN', 'DT', 'PRP', ':', 'WP$', 'NNPS', 'PRP$', 'WDT', '(', ')', '.', ',', '``', '$', 'RB', 'RBR', 'RBS', 'VBD', 'IN', 'FW', 'RP', 'JJR', 'JJS', 'PDT', 'MD', 'VB', 'WRB', 'NNP', 'EX', 'NNS', 'SYM', 'CC', 'CD', 'POS']
Based on Doug Shore’s method but make it more copy-paste friendly
Just run this verbatim.
import nltk nltk.download('tagsets') nltk.help.upenn_tagset()
nltk.tag._POS_TAGGER won’t work. It will give AttributeError: module ‘nltk.tag’ has no attribute ‘_POS_TAGGER’. It’s not available in NLTK 3 anymore.