スタンフォードNLPは、hereをデモを行っ次のような出力が得られます。Java Stanford NLP：音声ラベルの一部ですか？
Colorless/JJ green/JJ ideas/NNS sleep/VBP furiously/RB ./.
音声タグの一部は何を意味するのですか？私は公式のリストを見つけることができません。スタンフォード独自のシステムですか、普遍的なタグを使用していますか？ （例えば、JJとは何ですか？）
また、文章を繰り返して名詞を探すと、タグ.contains('N')を確認するようなことをします。これはかなり弱い感じです。プログラムによって特定の部分を検索するより良い方法はありますか？
The Penn Treebank Project。 Part-of-speech tagging psを見てください。
JJは形容詞です。 NNSは名詞、複数形です。 VBPは動詞である。 RBは副詞です。
これは英語用です。中国語では、ペン・チャイナ・ツリーバンクです。ドイツ語の場合はNEGRAコーパスです。
- CCコーディネート連動
- CDカーディナル数
- DTデタミナーが
- EX実存
- FW外国語
前置詞、IN- または従属接続詞
- JJの形容詞
- JJR形容詞、比較
- JJS形容詞単数、最上級
- LSリスト項目マーカー
- MDモーダル
- NN名詞、単数又は質量
- NNS名詞、複数
- NNP適切な名詞、
- NNPS固有名詞、複数
- PDTプレディクタイター
- POS固定終了
- PRP人称代名詞
- PRP $強欲代名詞
- RB副詞
- RBR副詞、比較
- RBS副詞、最上級
- RP粒子
- SYMシンボル
- TOへ
- UH中継
- VB動詞、基本形式
- VBD動詞、過去形
- VBG動詞、動名詞あるいは現在分詞
- VBN動詞、過去分詞
- VBP動詞、non3rd人
- 存在単数VBZ動詞、3人目単数現在
- WDT Whdeterminer
- WP whpronoun
- WP $強欲whpronoun
- WRB Whadverb
ちょうどあなたがそれをコーディングしたいた場合
Brown Corpus tagsと思われます。
いいえ、Penn English Treebank POSタグです。これはBrown Corpusタグセットの簡素化です。 –
本当ですか？上に引用した例には、 "。"というタグが含まれています。これはBrown Corpusで定義されていますが、上記のPenn Treebankタグのリストでは定義されていませんので、少なくともPenn Treebankタグのように簡単な答えではないと確信しています。 – Jules
追加の研究を行ったところ、* Penn Treebankタグであるように見えますが、上記タグで上に引用した文書は不完全です：Penn Treebankタグには、受け入れられた回答のリストから省略された9個の句読点タグも含まれています。詳細は私の追加の答えを見てください。 – Jules
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
あなたはソースを挙げてください。 –
句読点はどうですか？例えば、 '、'トークンはPoS '、'を取得します。これらのPoSを含むリストはありますか？ –
'（'トークンのPoS "-LRB-"についてはどうですか？ –
...
/**
* 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 + "'.");
}
}
ここでは、タグのより完全なリストですペンツリーバンク（completnessのためにここに掲載）のために：
http://www.surdeanu.info/mihai/teaching/ista555-fall13/readings/PennTreebankConstituents.html
また、句と句レベルのタグを含んでいます。
- 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
（リンクの説明）
これは人々が必要とする真のリストです.Penn Treebank POSタグだけでなく、言葉 –
私はここに全体のリストを提供していますし、また、参照リンクを与える
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
音声タグの部分の一覧は、hereです。ここで
あなたはかなりよくPOSタグと関係タグの説明を全体のトピックを説明しました：タグ付けされた（例えば、名詞）の単語/チャンクを特定のPOSを見つけることのあなたの2番目の質問については http://www.mathcs.emory.edu/~choi/doc/clear-dependency-2012.pdf
、ここにありますあなたが従うことができるサンプルコード。
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);
}
出力は次のようになります。
Input: Colorless green ideas sleep furiously.
Output: [ideas]
これはnitpickかもしれないが、あなたは 'IN' と以来、むしろcontains' 'より' .starts_with（ 'N'） '、および 'VBN' を使用する必要があります'N'も含む。そして、それはおそらく、タガーが名詞であると思う単語を見つける最良の方法です。 – Joseph
答えはすべてクールですが、これに対処できる人のために、各頭字語をいくつかの単語に分解する以外の意味を説明するコンパクトなリソースはありますが、下位接続詞のようなものの新しい記憶を保持していませんか？ – matanster