Java Stanford NLP: parte delle etichette vocali?

La Stanford NLP, demo’d qui , dà un risultato come questo:

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

Cosa significano i tag Part of Speech? Non riesco a trovare un elenco ufficiale. È il sistema di Stanford o usano tag universali? (Cos’è JJ , ad esempio?)

Inoltre, quando sto iterando attraverso le frasi, cercando nomi, per esempio, finisco per fare qualcosa come controllare per vedere se il tag .contains('N') . Questo sembra piuttosto debole. C’è un modo migliore per cercare programmaticamente una determinata parte del discorso?

Il progetto Penn Treebank . Guarda la codifica della parte del discorso ps.

JJ è aggettivo. NNS è sostantivo, plurale. VBP è tempo verbale presente. RB è avverbio.

Questo è per l’inglese. Per il cinese, è la Penn Chinese Treebank. E per il tedesco è il corpus NEGRA.

  1. CC Coordinating congiunzione
  2. Numero cardinale CD
  3. Determinatore DT
  4. EX Esistente lì
  5. FW Parola straniera
  6. IN Preposizione o subordinazione congiunzione
  7. JJ Adjective
  8. JJR Adjective, comparativo
  9. JJS Adjective, superlativo
  10. Indicatore di voce dell’elenco LS
  11. MD modale
  12. NN Noun, singolare o di massa
  13. NNS Noun, plurale
  14. NNP Nome proprio, singolare
  15. NNPS Nome proprio, plurale
  16. PDT Predeterminer
  17. POS Finale possente
  18. PRP Pronome personale
  19. PRP $ pronome possessivo
  20. RB Adverb
  21. RBR Adverb, comparativo
  22. RBS Adverb, superlativo
  23. Particella RP
  24. Simbolo SYM
  25. TO a
  26. Interiezione UH
  27. VB Verb, form base
  28. VBD Verb, passato
  29. VBG Verb, gerund o participio presente
  30. VBN Verb, participio passato
  31. Verbale VBP, presente singolare non terza persona
  32. VBZ Verb, presente singolare in terza persona
  33. WDT Whdeterminer
  34. WP Whpronoun
  35. WP $ Whitaund possessivo
  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 

La risposta accettata sopra manca le seguenti informazioni:

Sono inoltre definiti 9 tag di punteggiatura (che non sono elencati in alcuni riferimenti, vedi qui ). Questi sono:

  1. #
  2. $
  3. ” (usato per tutte le forms di preventivo di chiusura)
  4. ((usato per tutte le forms di parentesi aperta)
  5. ) (utilizzato per tutte le forms di parentesi di chiusura)
  6. ,
  7. . (usato per tutti i segni di punteggiatura che terminano le frasi)
  8. : (usato per due punti, punto e virgola ed ellissi)
  9. “ (usato per tutte le forms di preventivo di apertura)

Ecco una lista più completa di tag per la Penn Treebank (pubblicata qui per completezza):

Include anche i tag per i livelli di frase e frase.

Livello di clausola

 - S - SBAR - SBARQ - SINV - SQ 

Livello di frase


(descrizioni nel link)

Nel caso volessi codificarlo …

 /** * Represents the English parts-of-speech, encoded using the * de facto Penn Treebank * Project standard. * * @see Penn Treebank Specification */ 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 later in We can always come by later, 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. For 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 + "'." ); } } 

Fornisco qui l’elenco completo e fornisco anche un link di riferimento

 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 

Puoi trovare l’elenco completo dei tag Parts of Speech qui .

Per quanto riguarda la seconda domanda relativa alla ricerca di determinati POS (ad esempio, Noun) con tag / chunk, ecco il codice di esempio che è ansible seguire.

 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 sentences = annotation.get(CoreAnnotations.SentencesAnnotation.class); List output = new ArrayList<>(); String regex = "([{pos:/NN|NNS|NNP/}])"; //Noun for (CoreMap sentence : sentences) { List tokens = sentence.get(CoreAnnotations.TokensAnnotation.class); TokenSequencePattern pattern = TokenSequencePattern.compile(regex); TokenSequenceMatcher matcher = pattern.getMatcher(tokens); while (matcher.find()) { output.add(; } } System.out.println("Input: "+input); System.out.println("Output: "+output); } 

L’output è:

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

Sembrano essere tag di Brown Corpus .