Java Stanford NLP: Part of Speech labels?

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The Stanford NLP, demo'd here, gives an output like this:

Colorless/JJ green/JJ ideas/NNS sleep/VBP furiously/RB ./.

What do the Part of Speech tags mean? I am unable to find an official list. Is it Stanford's own system, or are they using universal tags? (What is JJ, for instance?)

Also, when I am iterating through the sentences, looking for nouns, for instance, I end up doing something like checking to see if the tag .contains('N'). This feels pretty weak. Is there a better way to programmatically search for a certain part of speech?

part-of-speech stanford-nlp nlp java
10 Answers
Explanation of each tag from the documentation :

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 ...
NNS: noun, common, plural
    undergraduates scotches bric-a-brac products bodyguards facets coasts
    divestitures storehouses designs clubs fragrances averages
    subjectivists apprehensions muses factory-jobs ...
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 ...
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
user_5532230

Stanford CoreNLP Tags for Other Languages : French, Spanish, German ...

I see you use the parser for English language, which is the default model. You may use the parser for other languages (French, Spanish, German ...) and, be aware, both tokenizers and part of speech taggers are different for each language. If you want to do that, you must download the specific model for the language (using a builder like Maven for example) and then set the model you want to use. Here you have more information about that.

Here you are lists of tags for different languages :

  1. Stanford CoreNLP POS Tags for Spanish
  2. Stanford CoreNLP POS Tagger for German uses the Stuttgart-Tübingen Tag Set (STTS)
  3. Stanford CoreNLP POS tagger for French uses the following tags:

TAGS FOR FRENCH:

Part of Speech Tags for French

A     (adjective)
Adv   (adverb)
CC    (coordinating conjunction)
Cl    (weak clitic pronoun)
CS    (subordinating conjunction)
D     (determiner)
ET    (foreign word)
I     (interjection)
NC    (common noun)
NP    (proper noun)
P     (preposition)
PREF  (prefix)
PRO   (strong pronoun)
V     (verb)
PONCT (punctuation mark)

Phrasal Categories Tags for French:

AP     (adjectival phrases)
AdP    (adverbial phrases)
COORD  (coordinated phrases)
NP     (noun phrases)
PP     (prepositional phrases)
VN     (verbal nucleus)
VPinf  (infinitive clauses)
VPpart (nonfinite clauses)
SENT   (sentences)
Sint, Srel, Ssub (finite clauses)

Syntactic Functions for French:

SUJ    (subject)
OBJ    (direct object)
ATS    (predicative complement of a subject)
ATO    (predicative complement of a direct object)
MOD    (modifier or adjunct)
A-OBJ  (indirect complement introduced by à)
DE-OBJ (indirect complement introduced by de)
P-OBJ  (indirect complement introduced by another preposition)
user_116805780

In spacy it was very fast i think, in just a low-end notebook it will run like this :

import spacy
import time

start = time.time()

with open('d:/dictionary/e-store.txt') as f:
    input = f.read()

word = 0
result = []

nlp = spacy.load("en_core_web_sm")
doc = nlp(input)

for token in doc:
    if token.pos_ == "NOUN":
        result.append(token.text)
    word += 1

elapsed = time.time() - start

print("From", word, "words, there is", len(result), "NOUN found in", elapsed, "seconds")

The Output in several trial :

From 3547 words, there is 913 NOUN found in 7.768507719039917 seconds
From 3547 words, there is 913 NOUN found in 7.408619403839111 seconds
From 3547 words, there is 913 NOUN found in 7.431427955627441 seconds

So, I think you don't need to worry about the looping for each POS tag check :)

More improvement I got when disabled certain pipeline :

nlp = spacy.load("en_core_web_sm", disable = 'ner')

So, The result is faster :

From 3547 words, there is 913 NOUN found in 6.212834596633911 seconds
From 3547 words, there is 913 NOUN found in 6.257707595825195 seconds
From 3547 words, there is 913 NOUN found in 6.371225833892822 seconds
user_132930150
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