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最佳答案

JJ 是形容词。 NNS 是名词，复数。 VBP 是动词现在时。 RB 是副词。

那是英语。中文是宾夕法尼亚大学的中文树木库。德文是 NEGRA 语料库。

协调连接

CD 基数

DT 测定器

前存在主义者

FW 外语词汇

介词或从属连词

JJ 形容词

形容词，比较

形容词，最高级

列表项目标记

海事处模态

名词，单数或质量

NNS 名词，复数

专有名词，单数

专有名词，复数

PDT 预决定器

占有结尾

PRP 人称代词

所有格代词

RB 副词

比较副词

苏格兰皇家银行副词，最高级

RP 粒子

SYM 符号

到

呃，打扰一下

VB 动词，基本形式

VBD 动词，过去式

动词，动名词或现在分词

动词，过去分词

动词，非第三人称单数现在

VBZ 动词，第三人称单数现在

WDT 卫星测距仪

WP 什么代词

所有格代词

WRB 什么动词

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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

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编号:

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

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以上所接受的答案缺少以下信息:

还定义了9个标点符号标记(在一些引用中没有列出，参见给你) ，它们是:

#
$
”(用于各种形式的结束语)
((用于所有形式的开括号)
)(用于所有形式的闭括号)
,
。(用于所有句子结束标点符号)
: (用于冒号、分号和省略号)
”(用于各种形式的开场白)

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下面是 Penn Treebank标签的一个更完整的列表(为了完整起见，张贴在这里) :

Http://www.surdeanu.info/mihai/teaching/ista555-fall13/readings/penntreebankconstituents.html

它还包括子句和短语级别的标记。

条款级别

- S
- SBAR
- SBARQ
- SINV
- SQ

短语水平

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

(详情请参阅连结)

小开

我在这里提供了整个列表，并给出了参考链接

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

你可以找到整个词类标签的列表给你。

小开

关于查找特定 POS (例如，Noun)标记的单词/块的第二个问题，下面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);
}

输出结果是:

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

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斯坦福 CoreNLP 标签为其他语言: 法语，西班牙语，德语..。

我看到您使用了用于英语语言的解析器，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，标记器和部分语音标记器对于每种语言都是不同的。如果您想这样做，您必须下载该语言的特定模型(例如使用 Maven 这样的构建器) ，然后设置您想要使用的模型。这里你有更多关于这方面的信息。

下面是不同语言的标签列表:

西班牙语的 POS 标签
Stanford CoreNLP POS Tagger for German 使用 < a href = “ https://www.ims.uni-stuttgart.de/forschung/ressource/lexika/TagSet/STTS-table.html”rel = “ nofollow noReferrer”> Stuttgart-Tübingen Tag Set (STTS)
Stanford CoreNLP 用于法语的 POS 标签使用以下标签:

法语标签:

法语部分词语标签

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)

短语分类法语标签:

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)

法语的句法功能:

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)

小开

在太空中，它的速度非常快，我认为，在一个低端笔记本电脑中，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")

几次试验的结果:

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

因此，我认为您不必担心每个 POS 标签检查的循环:)

关闭某些管道后，我得到了更多的改进:

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

因此，结果是更快的:

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

Java Stanford NLP: 语音标签的一部分？

条款级别

短语水平