首页 文章

Java Stanford NLP:部分语音标签?

提问于
浏览
156

斯坦福NLP,演示here,输出如下:

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

部分语音标签是什么意思?我无法找到正式名单 . 它是斯坦福自己的系统,还是使用通用标签? (例如 JJ 是什么?)

另外,例如,当我迭代句子,寻找名词时,我最终会做一些事情,比如查看标签 .contains('N') . 这感觉很弱 . 有没有更好的方法以编程方式搜索某个词性?

8 回答

  • 2
    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
    
  • 6

    以下是Penn Treebank的更完整标签列表(为了完整性而在此处发布):

    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
    

    短语等级

    - ADJP
    - ADVP
    - CONJP
    - FRAG
    - INTJ
    - LST
    - NAC
    - NP
    - NX
    - PP
    - PRN
    - PRT
    - QP
    - RRC
    - UCP
    - VP
    - WHADJP
    - WHAVP
    - WHNP
    - WHPP
    - X
    

    (链接中的描述)

  • 250

    关于你找到特定POS(例如,名词)标记的单词/块的第二个问题,这里是你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]
    
  • 4

    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
    

    您可以找到词性标签here的完整列表 .

  • 31

    上面接受的答案缺少以下信息:

    还定义了9个标点符号(某些参考文献中未列出,请参阅here) . 这些是:

    • $

    • ''(用于所有形式的收盘报价)

    • ((用于所有形式的左括号)

    • )(用于所有形式的右括号)

    • . (用于所有句子结尾的标点符号)

    • :(用于冒号,分号和椭圆)

    • ``(用于所有形式的开场报价)

  • 103

    The Penn Treebank Project . 看看Part-of-speech tagging ps .

    JJ是形容词 . NNS是名词,复数 . VBP是动词现在时态 . RB是副词 .

    这是英语 . 对于中国人来说,它是Penn Chinese Treebank . 而对于德国人来说,它是NEGRA语料库 .

    CC协调连接CD基数DT确定者EX存在有FW外来词IN介词或从属连词JJ形容词JJR形容词,比较JJS形容词,最高级LS列表项目标记MD模态NN名词,单数或质量NNS名词,复数NNP专有名词,单数NNPS专有名词,复数PDT Predeterminer POS占有结束PRP人称代词PRP $占有代词RB副词RBR副词,比较RBS副词,最高级RP粒子SYM符号TO到UH Interjection VB动词,基本形式VBD动词,过去时VBG动词,动名词或现在分词VBN Verb,过去分词VBP Verb,非第三人称单数礼物VBZ Verb,第三人称单数礼物WDT Whdeterminer WP Whpronoun WP $ Possessive whpronoun WRB Whadverb

  • 12

    他们似乎是Brown Corpus tags .

  • 16

    以防你想要编码...

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

相关问题