here 데모 된 Stanford NLP 는 다음과 같은 출력을 제공합니다.

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

품사 태그의 의미는 무엇입니까? 공식 목록을 찾을 수 없습니다. 스탠포드 자체 시스템입니까, 아니면 범용 태그를 사용하고 있습니까? ( JJ예를 들어 무엇입니까?)

또한 문장을 반복하고 명사를 찾을 때 태그가 있는지 확인하는 것과 같은 일을 끝내게됩니다 .contains('N'). 이것은 꽤 약한 느낌입니다. 말의 특정 부분을 프로그래밍 방식으로 검색하는 더 좋은 방법이 있습니까?

펜 트리 뱅크 프로젝트 . 상기 봐 품사 태깅 PS.

JJ는 형용사입니다. NNS는 명사, 복수입니다. VBP는 동사 시제입니다. RB는 부사입니다.

그것은 영어입니다. 중국인은 Penn Chinese Treebank입니다. 그리고 독일에게는 네 그라 코퍼스입니다.

1. CC 코디네이 팅 합동
2. CD 추기경 번호
3. DT 결정기
4. EX 존재
5. FW 외국어
6. IN 전치사 또는 대체 조합
7. JJ 형용사
8. JJR 형용사, 비교
9. JJS 형용사, 최상급
10. LS 목록 항목 마커
11. MD 모달
12. NN 명사, 단수 또는 질량
13. NNS 명사, 복수
14. NNP 고유 명사, 단수
15. NNPS 적절한 명사, 복수
16. PDT 사전 결정자
17. POS 소유 엔딩
18. PRP 개인 대명사
19. PRP $ 소유 대명사
20. RB 부사
21. RBR 부사, 비교
22. 최상급 RBS 부사
23. RP 입자
24. 기호
25. ~까지
26. UH 감청
27. VB 동사, 기본형
28. 과거 시제 VBD 동사
29. VBG 동사, 제럴드 또는 현재 분사
30. VBN 동사, 과거 분사
31. VBP 동사, 3 인이 아닌 단수 선물
32. VBZ 동사, 3 인칭 단수 선물
33. WDT Whdeterminer
34. WP 워프 론운
35. WP $ 소유 whpronoun
36. WRB Whadverb

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

위에 허용 된 답변에 다음 정보가 없습니다.

9 개의 문장 부호 태그도 정의되어 있습니다 (일부 참조에 나열되지 않음, 여기 참조 ). 이것들은:

1. #
2. $
3. ''(모든 형태의 마감 인용에 사용)
4. ((모든 형태의 여는 괄호에 사용)
5. ) (모든 형태의 닫는 괄호에 사용됨)
6. ,
7. . (모든 문장 끝 구두점에 사용됨)
8. : (콜론, 세미콜론 및 타원에 사용)
9. ``(모든 형태의 오프닝 견적에 사용)

다음은 Penn Treebank에 대한 전체 태그 목록입니다 (완료를 위해 게시 됨).

http://www.surdeanu.info/mihai/teaching/ista555-fall13/readings/PennTreebankConstituents.html

또한 절 및 구문 수준에 대한 태그도 포함합니다.

조항 수준

- S
- SBAR
- SBARQ
- SINV
- SQ

문구 수준

- ADJP
- ADVP
- CONJP
- FRAG
- INTJ
- LST
- NAC
- NP
- NX
- PP
- PRN
- PRT
- QP
- RRC
- UCP
- VP
- WHADJP
- WHAVP
- WHNP
- WHPP
- X

(링크 설명)

코드를 작성하려는 경우를 대비하여 ...

/**
 * 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 + "'." );
  }
}

여기에 전체 목록을 제공하고 참조 링크도 제공합니다

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

품사 태그의 전체 목록은 여기에서 확인할 수 있습니다 .

특정 POS (예 : 명사) 태그 단어 / 청크를 찾는 두 번째 질문과 관련하여 수행 할 수있는 샘플 코드는 다음과 같습니다.

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);
}

출력은 다음과 같습니다.

Input: Colorless green ideas sleep furiously.
Output: [ideas]

그들은 갈색 코퍼스 태그 인 것 같습니다 .

다른 언어에 대한 Stanford CoreNLP 태그 : 프랑스어, 스페인어, 독일어 ...

기본 모델 인 영어 파서를 사용하는 것이 보입니다. 다른 언어 (프랑스어, 스페인어, 독일어 ...)에 파서를 사용할 수 있으며 토크 나이저와 음성 태그의 일부는 언어마다 다릅니다. 그렇게하려면 언어에 대한 특정 모델 (예 : Maven과 같은 빌더 사용)을 다운로드 한 다음 사용하려는 모델을 설정해야합니다. 여기 더 많은 정보가 있습니다.

다음은 다양한 언어에 대한 태그 목록입니다.

1. 스페인어 용 Stanford CoreNLP POS 태그
2. 독일어 용 Stanford CoreNLP POS Tagger 는 스투 트가 르트 - 튀빙겐 태그 세트 (STTS를)
3. 프랑스어 용 Stanford CoreNLP POS 태거는 다음 태그를 사용합니다.

프랑스어 태그 :

프랑스어 음성 태그의 일부

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)

프랑스어에 대한 문구 카테고리 태그 :

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)

프랑스어 구문 기능 :

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)

spacy에서 그것은 매우 빠르다고 생각합니다. 저사양 노트북에서는 다음과 같이 실행될 것입니다.

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")

몇 차례의 시험 결과 :

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

따라서 각 POS 태그 검사의 반복에 대해 걱정할 필요가 없습니다. :)

특정 파이프 라인을 비활성화했을 때의 개선 사항 :

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

따라서 결과가 더 빠릅니다.

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