问题
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?
回答1:
The Penn Treebank Project. Look at the Part-of-speech tagging ps.
JJ is adjective. NNS is noun, plural. VBP is verb present tense. RB is adverb.
That's for english. For chinese, it's the Penn Chinese Treebank. And for german it's the NEGRA corpus.
- 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, non3rd person singular present
- VBZ Verb, 3rd person singular present
- WDT Whdeterminer
- WP Whpronoun
- WP$ Possessive whpronoun
- WRB Whadverb
回答2:
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
回答3:
The accepted answer above is missing the following information:
There are also 9 punctuation tags defined (which are not listed in some references, see here). These are:
- #
- $
- '' (used for all forms of closing quote)
- ( (used for all forms of opening parenthesis)
- ) (used for all forms of closing parenthesis)
- ,
- . (used for all sentence-ending punctuation)
- : (used for colons, semicolons and ellipses)
- `` (used for all forms of opening quote)
回答4:
Here is a more complete list of tags for the Penn Treebank (posted here for the sake of completness):
http://www.surdeanu.info/mihai/teaching/ista555-fall13/readings/PennTreebankConstituents.html
It also includes tags for clause and phrase levels.
Clause Level
- S
- SBAR
- SBARQ
- SINV
- SQ
Phrase Level
- ADJP
- ADVP
- CONJP
- FRAG
- INTJ
- LST
- NAC
- NP
- NX
- PP
- PRN
- PRT
- QP
- RRC
- UCP
- VP
- WHADJP
- WHAVP
- WHNP
- WHPP
- X
(descriptions in the link)
回答5:
Just in case you were wanting to code it...
/**
* Represents the English parts-of-speech, encoded using the
* de facto <a href="http://www.cis.upenn.edu/~treebank/">Penn Treebank
* Project</a> standard.
*
* @see <a href="ftp://ftp.cis.upenn.edu/pub/treebank/doc/tagguide.ps.gz">Penn Treebank Specification</a>
*/
public enum PartOfSpeech {
ADJECTIVE( "JJ" ),
ADJECTIVE_COMPARATIVE( ADJECTIVE + "R" ),
ADJECTIVE_SUPERLATIVE( ADJECTIVE + "S" ),
/* This category includes most words that end in -ly as well as degree
* words like quite, too and very, posthead modi ers like enough and
* indeed (as in good enough, very well indeed), and negative markers like
* not, n't and never.
*/
ADVERB( "RB" ),
/* Adverbs with the comparative ending -er but without a strictly comparative
* meaning, like <i>later</i> in <i>We can always come by later</i>, should
* simply be tagged as RB.
*/
ADVERB_COMPARATIVE( ADVERB + "R" ),
ADVERB_SUPERLATIVE( ADVERB + "S" ),
/* This category includes how, where, why, etc.
*/
ADVERB_WH( "W" + ADVERB ),
/* This category includes and, but, nor, or, yet (as in Y et it's cheap,
* cheap yet good), as well as the mathematical operators plus, minus, less,
* times (in the sense of "multiplied by") and over (in the sense of "divided
* by"), when they are spelled out. <i>For</i> in the sense of "because" is
* a coordinating conjunction (CC) rather than a subordinating conjunction.
*/
CONJUNCTION_COORDINATING( "CC" ),
CONJUNCTION_SUBORDINATING( "IN" ),
CARDINAL_NUMBER( "CD" ),
DETERMINER( "DT" ),
/* This category includes which, as well as that when it is used as a
* relative pronoun.
*/
DETERMINER_WH( "W" + DETERMINER ),
EXISTENTIAL_THERE( "EX" ),
FOREIGN_WORD( "FW" ),
LIST_ITEM_MARKER( "LS" ),
NOUN( "NN" ),
NOUN_PLURAL( NOUN + "S" ),
NOUN_PROPER_SINGULAR( NOUN + "P" ),
NOUN_PROPER_PLURAL( NOUN + "PS" ),
PREDETERMINER( "PDT" ),
POSSESSIVE_ENDING( "POS" ),
PRONOUN_PERSONAL( "PRP" ),
PRONOUN_POSSESSIVE( "PRP$" ),
/* This category includes the wh-word whose.
*/
PRONOUN_POSSESSIVE_WH( "WP$" ),
/* This category includes what, who and whom.
*/
PRONOUN_WH( "WP" ),
PARTICLE( "RP" ),
/* This tag should be used for mathematical, scientific and technical symbols
* or expressions that aren't English words. It should not used for any and
* all technical expressions. For instance, the names of chemicals, units of
* measurements (including abbreviations thereof) and the like should be
* tagged as nouns.
*/
SYMBOL( "SYM" ),
TO( "TO" ),
/* This category includes my (as in M y, what a gorgeous day), oh, please,
* see (as in See, it's like this), uh, well and yes, among others.
*/
INTERJECTION( "UH" ),
VERB( "VB" ),
VERB_PAST_TENSE( VERB + "D" ),
VERB_PARTICIPLE_PRESENT( VERB + "G" ),
VERB_PARTICIPLE_PAST( VERB + "N" ),
VERB_SINGULAR_PRESENT_NONTHIRD_PERSON( VERB + "P" ),
VERB_SINGULAR_PRESENT_THIRD_PERSON( VERB + "Z" ),
/* This category includes all verbs that don't take an -s ending in the
* third person singular present: can, could, (dare), may, might, must,
* ought, shall, should, will, would.
*/
VERB_MODAL( "MD" ),
/* Stanford.
*/
SENTENCE_TERMINATOR( "." );
private final String tag;
private PartOfSpeech( String tag ) {
this.tag = tag;
}
/**
* Returns the encoding for this part-of-speech.
*
* @return A string representing a Penn Treebank encoding for an English
* part-of-speech.
*/
public String toString() {
return getTag();
}
protected String getTag() {
return this.tag;
}
public static PartOfSpeech get( String value ) {
for( PartOfSpeech v : values() ) {
if( value.equals( v.getTag() ) ) {
return v;
}
}
throw new IllegalArgumentException( "Unknown part of speech: '" + value + "'." );
}
}
回答6:
I am providing the whole list here and also giving reference link
1. CC Coordinating conjunction
2. CD Cardinal number
3. DT Determiner
4. EX Existential there
5. FW Foreign word
6. IN Preposition or subordinating conjunction
7. JJ Adjective
8. JJR Adjective, comparative
9. JJS Adjective, superlative
10. LS List item marker
11. MD Modal
12. NN Noun, singular or mass
13. NNS Noun, plural
14. NNP Proper noun, singular
15. NNPS Proper noun, plural
16. PDT Predeterminer
17. POS Possessive ending
18. PRP Personal pronoun
19. PRP$ Possessive pronoun
20. RB Adverb
21. RBR Adverb, comparative
22. RBS Adverb, superlative
23. RP Particle
24. SYM Symbol
25. TO to
26. UH Interjection
27. VB Verb, base form
28. VBD Verb, past tense
29. VBG Verb, gerund or present participle
30. VBN Verb, past participle
31. VBP Verb, non-3rd person singular present
32. VBZ Verb, 3rd person singular present
33. WDT Wh-determiner
34. WP Wh-pronoun
35. WP$ Possessive wh-pronoun
36. WRB Wh-adverb
You can find out the whole list of Parts of Speech tags here.
回答7:
Regarding your second question of finding particular POS (e.g., Noun) tagged word/chunk, here is the sample code you can follow.
public static void main(String[] args) {
Properties properties = new Properties();
properties.put("annotators", "tokenize, ssplit, pos, lemma, ner, parse");
StanfordCoreNLP pipeline = new StanfordCoreNLP(properties);
String input = "Colorless green ideas sleep furiously.";
Annotation annotation = pipeline.process(input);
List<CoreMap> sentences = annotation.get(CoreAnnotations.SentencesAnnotation.class);
List<String> output = new ArrayList<>();
String regex = "([{pos:/NN|NNS|NNP/}])"; //Noun
for (CoreMap sentence : sentences) {
List<CoreLabel> tokens = sentence.get(CoreAnnotations.TokensAnnotation.class);
TokenSequencePattern pattern = TokenSequencePattern.compile(regex);
TokenSequenceMatcher matcher = pattern.getMatcher(tokens);
while (matcher.find()) {
output.add(matcher.group());
}
}
System.out.println("Input: "+input);
System.out.println("Output: "+output);
}
The output is:
Input: Colorless green ideas sleep furiously.
Output: [ideas]
回答8:
They seem to be Brown Corpus tags.
回答9:
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 :
- Stanford CoreNLP POS Tags for Spanish
- Stanford CoreNLP POS Tagger for German uses the Stuttgart-Tübingen Tag Set (STTS)
- 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)
来源：https://stackoverflow.com/questions/1833252/java-stanford-nlp-part-of-speech-labels