nltk词性标注的涵义

这学期nlp一个实验要求使用brown语料库，在搜索其词性标注含义的时候发现nltk词性标注的缩写应该有好几种规范，可以使用ntlk.help模块进行查看其涵义，help.py中部分代码如下：

def brown_tagset(tagpattern=None):_format_tagset("brown_tagset", tagpattern)def claws5_tagset(tagpattern=None):_format_tagset("claws5_tagset", tagpattern)def upenn_tagset(tagpattern=None):_format_tagset("upenn_tagset", tagpattern)

ntlk.help.brown_tagset()

brown语料库的标注很多，名词有NN，NNS，NP，NPS

(: opening parenthesis(
): closing parenthesis)
*: negatornot n't
,: comma,
--: dash--
.: sentence terminator. ? ; ! :
:: colon:
ABL: determiner/pronoun, pre-qualifierquite such rather
ABN: determiner/pronoun, pre-quantifierall half many nary
ABX: determiner/pronoun, double conjunction or pre-quantifierboth
AP: determiner/pronoun, post-determinermany other next more last former little several enough most least onlyvery few fewer past same Last latter less single plenty 'nough lessercertain various manye next-to-last particular final previous presentnuf
AP$: determiner/pronoun, post-determiner, genitiveother's
AP+AP: determiner/pronoun, post-determiner, hyphenated pairmany-much
AT: articlethe an no a every th' ever' ye
BE: verb 'to be', infinitive or imperativebe
BED: verb 'to be', past tense, 2nd person singular or all persons pluralwere
BED*: verb 'to be', past tense, 2nd person singular or all persons plural, negatedweren't
BEDZ: verb 'to be', past tense, 1st and 3rd person singularwas
BEDZ*: verb 'to be', past tense, 1st and 3rd person singular, negatedwasn't
BEG: verb 'to be', present participle or gerundbeing
BEM: verb 'to be', present tense, 1st person singularam
BEM*: verb 'to be', present tense, 1st person singular, negatedain't
BEN: verb 'to be', past participlebeen
BER: verb 'to be', present tense, 2nd person singular or all persons pluralare art
BER*: verb 'to be', present tense, 2nd person singular or all persons plural, negatedaren't ain't
BEZ: verb 'to be', present tense, 3rd person singularis
BEZ*: verb 'to be', present tense, 3rd person singular, negatedisn't ain't
CC: conjunction, coordinatingand or but plus & either neither nor yet 'n' and/or minus an'
CD: numeral, cardinaltwo one 1 four 2 1913 71 74 637 1937 8 five three million 87-31 29-5seven 1,119 fifty-three 7.5 billion hundred 125,000 1,700 60 100 six...
CD$: numeral, cardinal, genitive1960's 1961's .404's
CS: conjunction, subordinatingthat as after whether before while like because if since for than althountil so unless though providing once lest s'posin' till whereaswhereupon supposing tho' albeit then so's 'fore
DO: verb 'to do', uninflected present tense, infinitive or imperativedo dost
DO*: verb 'to do', uninflected present tense or imperative, negateddon't
DO+PPSS: verb 'to do', past or present tense + pronoun, personal, nominative, not 3rd person singulard'you
DOD: verb 'to do', past tensedid done
DOD*: verb 'to do', past tense, negateddidn't
DOZ: verb 'to do', present tense, 3rd person singulardoes
DOZ*: verb 'to do', present tense, 3rd person singular, negateddoesn't don't
DT: determiner/pronoun, singularthis each another that 'nother
DT$: determiner/pronoun, singular, genitiveanother's
DT+BEZ: determiner/pronoun + verb 'to be', present tense, 3rd person singularthat's
DT+MD: determiner/pronoun + modal auxillarythat'll this'll
DTI: determiner/pronoun, singular or pluralany some
DTS: determiner/pronoun, pluralthese those them
DTS+BEZ: pronoun, plural + verb 'to be', present tense, 3rd person singularthem's
DTX: determiner, pronoun or double conjunctionneither either one
EX: existential therethere
......省略

nltk.help.upenn_tagset()

名词有NN，NNS，NNP，NNPS（专有名词和brown不一样）

$: dollar$ -$ --$ A$ C$ HK$ M$ NZ$ S$ U.S.$ US$
'': closing quotation mark' ''
(: opening parenthesis( [ {
): closing parenthesis) ] }
,: comma,
--: dash--
.: sentence terminator. ! ?
:: colon or ellipsis: ; ...
CC: conjunction, coordinating& 'n and both but either et for less minus neither nor or plus sotherefore times v. versus vs. whether yet
CD: numeral, cardinalmid-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 .025fifteen 271,124 dozen quintillion DM2,000 ...
DT: determinerall an another any both del each either every half la many much naryneither no some such that the them these this those
EX: existential therethere
FW: foreign wordgemeinschaft hund ich jeux habeas Haementeria Herr K'ang-si vouslutihaw alai je jour objets salutaris fille quibusdam pas trop Monteterram fiche oui corporis ...
IN: preposition or conjunction, subordinatingastride among uppon whether out inside pro despite on by throughoutbelow within for towards near behind atop around if like until belownext into if beside ...
JJ: adjective or numeral, ordinalthird ill-mannered pre-war regrettable oiled calamitous first separableectoplasmic battery-powered participatory fourth still-to-be-namedmultilingual multi-disciplinary ...
JJR: adjective, comparativebleaker braver breezier briefer brighter brisker broader bumper busiercalmer cheaper choosier cleaner clearer closer colder commoner costliercozier creamier crunchier cuter ...
JJS: adjective, superlativecalmest cheapest choicest classiest cleanest clearest closest commonestcorniest costliest crassest creepiest crudest cutest darkest deadliestdearest deepest densest dinkiest ...
LS: list item markerA A. B B. C C. D E F First G H I J K One SP-44001 SP-44002 SP-44005SP-44007 Second Third Three Two * a b c d first five four one six threetwo
MD: modal auxiliarycan cannot could couldn't dare may might must need ought shall shouldshouldn't will would
NN: noun, common, singular or masscommon-carrier cabbage knuckle-duster Casino afghan shed thermostatinvestment slide humour falloff slick wind hyena override subhumanitymachinist ...
NNP: noun, proper, singularMotown Venneboerger Czestochwa Ranzer Conchita Trumplane ChristosOceanside Escobar Kreisler Sawyer Cougar Yvette Ervin ODI Darryl CTCAShannon A.K.C. Meltex Liverpool ...
NNPS: noun, proper, pluralAmericans Americas Amharas Amityvilles Amusements Anarcho-SyndicalistsAndalusians Andes Andruses Angels Animals Anthony Antilles AntiquesApache Apaches Apocrypha ...
NNS: noun, common, pluralundergraduates scotches bric-a-brac products bodyguards facets coastsdivestitures storehouses designs clubs fragrances averagessubjectivists apprehensions muses factory-jobs ...
PDT: pre-determinerall both half many quite such sure this
POS: genitive marker' 's
PRP: pronoun, personalhers herself him himself hisself it itself me myself one oneself oursourselves ownself self she thee theirs them themselves they thou thy us
PRP$: pronoun, possessiveher his mine my our ours their thy your
RB: adverboccasionally unabatingly maddeningly adventurously professedlystirringly prominently technologically magisterially predominatelyswiftly fiscally pitilessly ...
RBR: adverb, comparativefurther gloomier grander graver greater grimmer harder harsherhealthier heavier higher however larger later leaner lengthier less-perfectly lesser lonelier longer louder lower more ...
RBS: adverb, superlativebest biggest bluntest earliest farthest first furthest hardestheartiest highest largest least less most nearest second tightest worst
RP: particleaboard about across along apart around aside at away back before behindby crop down ever fast for forth from go high i.e. in into just laterlow more off on open out over per pie raising start teeth that throughunder unto up up-pp upon whole with you
SYM: symbol% & ' '' ''. ) ). * + ,. < = > @ A[fj] U.S U.S.S.R * ** ***
TO: "to" as preposition or infinitive markerto
UH: interjectionGoodbye Goody Gosh Wow Jeepers Jee-sus Hubba Hey Kee-reist Oops amenhuh howdy uh dammit whammo shucks heck anyways whodunnit honey gollyman baby diddle hush sonuvabitch ...
VB: verb, base formask assemble assess assign assume atone attention avoid bake balkanizebank begin behold believe bend benefit bevel beware bless boil bombboost brace break bring broil brush build ...
VBD: verb, past tensedipped pleaded swiped regummed soaked tidied convened halted registeredcushioned exacted snubbed strode aimed adopted belied figgeredspeculated wore appreciated contemplated ...
VBG: verb, present participle or gerundtelegraphing stirring focusing angering judging stalling lactatinghankerin' alleging veering capping approaching traveling besiegingencrypting interrupting erasing wincing ...
VBN: verb, past participlemultihulled dilapidated aerosolized chaired languished panelized usedexperimented flourished imitated reunifed factored condensed shearedunsettled primed dubbed desired ...
VBP: verb, present tense, not 3rd person singularpredominate wrap resort sue twist spill cure lengthen brush terminateappear tend stray glisten obtain comprise detest tease attractemphasize mold postpone sever return wag ...
VBZ: verb, present tense, 3rd person singularbases reconstructs marks mixes displeases seals carps weaves snatchesslumps stretches authorizes smolders pictures emerges stockpilesseduces fizzes uses bolsters slaps speaks pleads ...
WDT: WH-determinerthat what whatever which whichever
WP: WH-pronounthat what whatever whatsoever which who whom whosoever
WP$: WH-pronoun, possessivewhose
WRB: Wh-adverbhow however whence whenever where whereby whereever wherein whereof why
``: opening quotation mark

nltk词性标注的涵义相关推荐

NLP5：NLTK词性标注
实验五 NLTK词性标注一.实验内容二.实验步骤 1.将字符串使用由标识符和标记组成的元组来表示 2.读入经过标注布朗语料库,并将单词的词性映射到简化的标记集. 3.统计布朗新闻语料库中词性的搭配 ...
nltk词性标注与词形还原中的词性类型匹配
一.两函数词性不匹配问题: nltk中词性标注函数nltk.pos_tag([])得到的结果:NN,NNP,VB等 nltk中词形还原函数nltk.stem.WordNetLemmatizer().l ...
nltk词性标注英文简称及分类
标注词表: 名词:NN,NNS,NNP,NNPS 代词:PRP,PRP$ 形容词:JJ,JJR,JJS 数词:CD 动词:VB,VBD,VBG,VBN,VBP,VBZ 副词:RB,RBR,RBS 1. ...
Python 数据科学入门教程：NLTK
自然语言处理教程原文:Natural Language Process 译者:飞龙协议:CC BY-NC-SA 4.0 一.使用 NLTK 分析单词和句子欢迎阅读自然语言处理系列教程,使用 Py ...
词性标注集句和句法分析标注集
两大标注集说明通用词性标签(Universal POS tags) NLTK词性标注集一个简单的概括以字母顺序展开解释句法分析树标注集依存句法CoNLL-U 格式(CoNLL2014) 参 ...
自然语言处理（二）——词性标注与命名实体识别
文章目录一.Jieba 二.NLPIR 三.nltk 四.SnowNLP 五.StandFordNLP 六.thulac 七.StandfordNLP 八.结论本次依然使用上篇博客( 自然语言处理 ...
Python【jieba】词性标注表
文章目录 jieba词性标注表(0.39版) 读取jieba词库,生成词性标注表,保存为excel 带词性的分词词与词性间映射 en2cn字典映射 0.42版后paddle模式词性和专名类别标签其 ...
英文文本分词处理（NLTK）
文章目录 1.NLTK的安装 2.NLTK分词和分句 3.NLTK分词后去除标点符号 4.NLTK分词后去除停用词 5.NLTK分词后进行词性标注 6.NLTK分词后进行词干提取 7.NLTK分词后进 ...
NLP学习（六）-词性标注问题
nltk词性标注词性对照 1.词性标注器 #词性标注器 text = word_tokenize("今天的天气是真的好苹果") print(pos_tag(text)) ...
python自然语言处理入门教程（一）
1.NLTK自然语言处理工具包 1.1 NLTK简介 NLTK是构建Python程序与人类语言数据工作的主要平台.它提供了易于使用的界面,以超过50语料库和词汇资源,如WordNet的,连同一套文字处 ...

nltk词性标注的涵义

nltk词性标注的涵义相关推荐

最新文章

热门文章