[英]Java Stanford NLP: Part of Speech labels?
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?词性标签是什么意思? I am unable to find an official list.
我找不到官方列表。 Is it Stanford's own system, or are they using universal tags?
是斯坦福自己的系统,还是他们使用通用标签? (What is
JJ
, for instance?) (例如,什么是
JJ
?)
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')
.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')
。 This feels pretty weak.这感觉很弱。 Is there a better way to programmatically search for a certain part of speech?
有没有更好的方法来以编程方式搜索某个词性?
The Penn Treebank Project . 宾夕法尼亚州树库项目。 Look at the Part-of-speech tagging ps.
查看词性标注ps。
JJ is adjective. JJ是形容词。 NNS is noun, plural.
NNS 是名词,复数。 VBP is verb present tense.
VBP 是动词现在时。 RB is adverb.
RB 是副词。
That's for english.那是为英语。 For chinese, it's the Penn Chinese Treebank.
对于中国人来说,这是宾夕法尼亚大学的中国树库。 And for german it's the NEGRA corpus.
对于德语,它是 NEGRA 语料库。
- CC Coordinating conjunction
CC 协调连词
- CD Cardinal number
CD 基数
- DT Determiner
DT 确定器
- EX Existential there
EX 存在那里
- FW Foreign word
FW 外来词
- IN Preposition or subordinating conjunction
IN 介词或从属连词
- JJ Adjective
JJ形容词
- JJR Adjective, comparative
JJR 形容词,比较级
- JJS Adjective, superlative
JJS 形容词,最高级
- LS List item marker
LS 列表项标记
- MD Modal
MD 模态
- NN Noun, singular or mass
NN 名词,单数或大量
- NNS Noun, plural
NNS 名词,复数
- NNP Proper noun, singular
NNP 专有名词,单数
- NNPS Proper noun, plural
NNPS 专有名词,复数
- PDT Predeterminer
PDT 预定器
- POS Possessive ending
POS 占有式结局
- PRP Personal pronoun
PRP 人称代词
- PRP$ Possessive pronoun
PRP$ 物主代词
- RB Adverb
RB 副词
- RBR Adverb, comparative
RBR 副词,比较级
- RBS Adverb, superlative
RBS 副词,最高级
- RP Particle
反相粒子
- SYM Symbol
符号
- TO to
到
- UH Interjection
呃感叹词
- VB Verb, base form
VB 动词,基本形式
- VBD Verb, past tense
VBD 动词,过去时
- VBG Verb, gerund or present participle
VBG 动词、动名词或现在分词
- VBN Verb, past participle
VBN 动词,过去分词
- VBP Verb, non3rd person singular present
VBP 动词,非第三人称单数现在时
- VBZ Verb, 3rd person singular present
VBZ 动词,第三人称单数现在时
- WDT Whdeterminer
WDT Wh确定器
- WP Whpronoun
WP 代词
- WP$ Possessive whpronoun
WP$ 所有格代词
- WRB Whadverb
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
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 ).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)。 These are:
这些是:
Here is a more complete list of tags for the Penn Treebank (posted here for the sake of completness):这是Penn Treebank 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。
- S
- SBAR
- SBARQ
- SINV
- SQ
- 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)
Codified:编纂:
/**
* 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 + "'." );
}
}
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。
Regarding your second question of finding particular POS (eg, 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]
它们似乎是Brown Corpus 的标签。
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.我看到你使用英语语言的解析器,这是默认模型。 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.
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。 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.
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。 Here you have more information about that.
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:
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)
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
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