Java斯坦福大学:语音部分的标签吗?
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11-09-2019 - |
题
斯坦福大学演示会 在这里,, 给输出这样的:
Colorless/JJ green/JJ ideas/NNS sleep/VBP furiously/RB ./.
什么语音部分标记的意思吗?我无法找到一个官方的名单。这是斯坦福大学的自己的系统,或被他们使用的通用标签?(是什么 JJ
, 例如?)
此外,当我迭代过句,在寻找名词,例如,我结束了在做喜欢的东西来看看标签 .contains('N')
.这感觉很薄弱。是否有更好的办法来编程方式搜索的某些部分的语音?
解决方案
JJ是形容词。NNS是名词、复数。E是动词目前的紧张。RB是副词。
那是英语。对于中国,它是宾夕法尼亚中文句法.并且对德国是NEGRA料库。
- CC协调结合
- CD基数
- DT确定器
- 前存在的有
- F外字
- 在介词或从属结合
- JJ形容词
- JJR形容词,比较
- JJS形容词,最高级的
- LS列项目的标记
- MD模式
- NN名词,单数或质量
- NNS名词的复数
- NNP适当的名词,奇异
- NNPS适当的名词、复数
- PDT Predeterminer
- POS占有结束
- PRP人称代词
- PRP$占有的代名词
- RB副词
- "全面彻底审查"副词,比较
- 苏格兰皇家银行副词,最高级的
- RP粒
- SYM符号
- 来到
- 呃感叹词
- VB动词,基本形式
- 自愿献血的动词,过去的紧张
- VBG动词、名词或本分词
- 购买词,过去分词
- E动词,non3rd人奇本
- VBZ动词,第3人奇本
- WDT Whdeterminer
- WP Whpronoun
- WP$占有欲whpronoun
- 战时难民事务委员会Whadverb
其他提示
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
所接受的回答上述缺失以下信息:
还有9标点符号标签的定义(其中未列出的一些参考资料,请参阅 在这里,).这些是:
- #
- $
- "(用于所有形式的关闭报价)
- ((用于所有形式的括号)
- )(用于所有形式的右括号)
- ,
- .(用于所有刑期结束的标点符号)
- :(用于冒号,分号和省略号)
- `(用于所有形式的开放报价)
下面是标签为一个更完整的清单中的宾州树库的(这里张贴completness的缘故):
http://www.surdeanu.info/mihai/teaching/ista555-fall13 /readings/PennTreebankConstituents.html
它还包括标签为子句和短语的水平。
第等级
- S
- SBAR
- SBARQ
- SINV
- SQ
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
(在链接描述)
万一你想编写它...
/**
* 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 + "'." );
}
}
我在这里提供整个清单,并给予参考链接
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
您可以找出语音标签这里
关于你的查找特定POS(例如,名词)标记的字/数据块的第二个问题,这里是示例代码可以按照
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);
}
的输出是:
Input: Colorless green ideas sleep furiously.
Output: [ideas]
他们似乎布朗语料库标记。
斯坦福大学CoreNLP标签的其他语言:法语、德语...
我看到你用的分析程序用英语语言,这是默认的模型。你可以使用的分析程序用于其他语文(法语、德语...),要知道,这两个系统和一部分的语音注的是对每一个不同的语言。如果你想做那个,你必须下载的具体模式语文(使用一个建设者像家为例),然后设置的模型的你想要来使用。在这里, 你有更多的信息。
你在这里列出的标签用于不同的语言:
- 斯坦福大学CoreNLP POS标签用于西班牙
- 斯坦福大学CoreNLP POS恶搞为德国 使用 斯图加特Tübingen的标签设置的(限制)
- 斯坦福大学CoreNLP POS标注为法国的使用下列标记:
标签用于法国:
一部分的语音标记,法国
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)
短语的类别标记,用于法国:
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)
句法职能对于法国:
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)