爪哇斯坦福大学NLP：语音标签的一部分？

这里演示的斯坦福大学的NLP给出了这样的输出结果： 

Colorless/JJ green/JJ ideas/NNS sleep/VBP furiously/RB ./.

词性标签是什么意思？ 我无法find正式名单。 是斯坦福自己的系统，还是使用通用标签？ （例如JJ是什么？）

而且，当我遍历句子，例如寻找名词时，我最终做了一些事情，比如检查标签是否.contains('N') 。 这感觉很弱。 有没有更好的方式来以编程方式search某个词类？

Penn Treebank项目 。 看看词性标注 ps。

JJ是形容词。 NNS是名词，复数。 VBP是动词现在式。 RB是副词。

这是英文。 对于中国人来说，这是宾州中国的树库。 对于德国人来说，这是NEGRA语料库。

  1. CC协调连接
  2. CD基数
  3. DT确定
  4. EX在那里存在
  5. FW外国词
  6. 在介词或从属连词
  7. JJ形容词
  8. JJR形容词，比较
  9. JJS形容词，最高级
  10. LS列表项目标记
  11. MD Modal
  12. NN名词，单数或大量
  13. NNS名词，复数
  14. NNP适当的名词，单数
  15. NNPS适当的名词，复数
  16. PDT预定义
  17. POS拥有结局
  18. PRP人称代词
  19. PRP $拥有代名词
  20. RB副词
  21. RBR副词，比较
  22. RBS副词，最高级
  23. RP粒子
  24. SYM符号
  25. TO到
  26. UH感叹号
  27. VB动词，基本forms
  28. VBD动词，过去式
  29. VBG动词，动名词或现在分词
  30. VBN动词，过去分词
  31. VBP动词，非第三人称单数存在
  32. VBZ动词，第三人称单数存在
  33. WDT Whdeterminer
  34. WP Whpronoun
  35. WP $拥有whpronoun
  36. 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. DEF First GHIJK One SP-44001 SP-44002 SP-44005 SP-44007 Second Third Three Two * abcd 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 AKC 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 ie 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] US USSR * ** *** 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

上面接受的答案是缺less以下信息：

还定义了9个标点符号（在某些参考文献中未列出），请参阅此处 。 这些是：

  1. ＃
  2. $
  3. ''（用于所有forms的结束报价）
  4. （（用于所有forms的左括号）
  5. ）（用于所有forms的右括号）
  6. ，
  7. 。 （用于所有句尾标点符号）
  8. :(用于冒号，分号和省略号）
  9. “（用于所有forms的开幕报价）

下面是Penn Treebank的一个更完整的标签列表（贴在这里为了完整性）：

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

它还包括子句和短语级别的标签。

条款级别 

 - 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

（在链接中的描述）

以防万一你想要编码… 

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

我在这里提供整个列表，也给参考链接 

 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

你可以在这里find“词性标记”的整个列表。

关于find特定POS（例如，名词）标记的单词/块的第二个问题，以下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); }

输出是： 

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

他们似乎是布朗语料库标签 。

在这里，你已经很好地解释了与POS标签和关系标签的描述的整个主题： http : //www.mathcs.emory.edu/~choi/doc/clear-dependency-2012.pdf

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