Java 斯坦福 NLP：语音标签的一部分？

Java Stanford NLP: Part of Speech labels?

提问by Nick Heiner

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

此处演示的斯坦福 NLP给出了如下输出：

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?)

词性标签是什么意思？我找不到官方列表。是斯坦福自己的系统，还是他们使用通用标签？（JJ例如，什么是？）

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?

此外，当我遍历句子时，例如寻找名词时，我最终会做一些事情，例如检查标签.contains('N'). 这感觉很弱。有没有更好的方法来以编程方式搜索某个词性？

采纳答案by anno

The Penn Treebank Project. Look at the Part-of-speech taggingps.

宾夕法尼亚州树库项目。查看词性标注ps。

JJ is adjective. NNS is noun, plural. VBP is verb present tense. RB is adverb.

JJ是形容词。NNS 是名词，复数。VBP 是动词现在时。RB 是副词。

That's for english. For chinese, it's the Penn Chinese Treebank. And for german it's the NEGRA corpus.

那是为英语。对于中国人来说，这是宾夕法尼亚大学的中国树库。对于德语，它是 NEGRA 语料库。

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

1. CC 协调连词
2. CD 基数
3. DT 确定器
4. EX 存在那里
5. FW 外来词
6. IN 介词或从属连词
7. JJ形容词
8. JJR 形容词，比较级
9. JJS 形容词，最高级
10. LS 列表项标记
11. MD 模态
12. NN 名词，单数或大量
13. NNS 名词，复数
14. NNP 专有名词，单数
15. NNPS 专有名词，复数
16. PDT 预定器
17. POS 占有式结局
18. PRP 人称代词
19. PRP$ 物主代词
20. RB 副词
21. RBR 副词，比较级
22. RBS 副词，最高级
23. 反相粒子
24. 符号
25. 到
26. 呃感叹词
27. VB 动词，基本形式
28. VBD 动词，过去时
29. VBG 动词、动名词或现在分词
30. VBN 动词，过去分词
31. VBP 动词，非第三人称单数现在时
32. VBZ 动词，第三人称单数现在时
33. WDT Wh 决定器
34. WP Wh-代词
35. WP$ 所有格 wh 代词
36. WRB Wh-副词

回答by Jonathan Feinberg

They seem to be Brown Corpus tags.

它们似乎是Brown Corpus 的标签。

回答by vaichidrewar

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by Dave Jarvis

Just in case you were wanting to code it...

以防万一你想编码......

/**
 * 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by Jules

The accepted answer above is missing the following information:

上面接受的答案缺少以下信息：

There are also 9 punctuation tags defined (which are not listed in some references, see here). These are:

还定义了 9 个标点符号（在某些参考资料中未列出，请参阅此处）。这些是：

1. #
2. $
3. '' (used for all forms of closing quote)
4. ( (used for all forms of opening parenthesis)
5. ) (used for all forms of closing parenthesis)
6. ,
7. . (used for all sentence-ending punctuation)
8. : (used for colons, semicolons and ellipses)
9. `` (used for all forms of opening quote)

1. #
2. $
3. ''（用于所有形式的结束语）
4. ( (用于所有形式的左括号)
5. )（用于所有形式的右括号）
6. ,
7. . （用于所有句子结尾的标点符号）
8. :（用于冒号、分号和省略号）
9. ``（用于所有形式的开场白）

回答by Iulius Curt

Here is a more complete list of tags for the Penn Treebank(posted here for the sake of completness):

这是PennTreebank的更完整的标签列表（为了完整起见，在此处发布）：

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

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

It also includes tags for clause and phrase levels.

它还包括子句和短语级别的标签。

Clause Level

条款级别

- S
- SBAR
- SBARQ
- SINV
- SQ

Phrase Level

短语级别

- 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)

（链接中的说明）

回答by Sri

I am providing the whole list here and also giving reference link

我在这里提供整个列表并提供参考链接

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.

您可以在此处找到词性标签的完整列表。

回答by Ashok Kumar Pant

Regarding your second question of finding particular POS (e.g., Noun) tagged word/chunk, here is the sample code you can follow.

关于查找特定 POS（例如，名词）标记的单词/块的第二个问题，这里是您可以遵循的示例代码。

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:

输出是：

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

回答by Catalina Chircu

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

其他语言的斯坦福 CoreNLP 标签：法语、西班牙语、德语......

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. Hereyou have more information about that.

我看到你使用英语语言的解析器，这是默认模型。您可以将解析器用于其他语言（法语、西班牙语、德语...），请注意，每种语言的分词器和词性标注器都不同。如果你想这样做，你必须下载语言的特定模型（例如使用像 Maven 这样的构建器），然后设置你想要使用的模型。 在这里你有更多关于这方面的信息。

Here you are lists of tags for different languages :

这是不同语言的标签列表：

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

1. 西班牙语的斯坦福 CoreNLP POS 标签
2. 适用于德语的斯坦福 CoreNLP POS标记器使用Stuttgart-Tübingen 标记集 (STTS)
3. 斯坦福 CoreNLP 法语词性标注器使用以下标签：

TAGS FOR FRENCH:

法语标签：

Part of Speech Tags for French

法语的词性标签

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:

法语短语类别标签：

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:

法语的句法函数：

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)

回答by Syauqi Haris

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

我认为在 spacy 中它非常快，在低端笔记本中它会像这样运行：

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 :

几次试验的输出：

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 :)

因此，我认为您无需担心每个 POS 标签检查的循环：)

More improvement I got when disabled certain pipeline :

禁用某些管道时我得到了更多改进：

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

So, The result is faster :

所以，结果更快：

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