For English vocabulary analysis and sentence analysis in natural language, model trainin

Overview

Sword Come

🌈 For English vocabulary analysis and sentence analysis in natural language, model training, intelligent response and emotion analysis reasoning are carried out through the analysis of data :)

📌 FAQ

Stanford part of speech description
ROOT:要处理文本的语句
IP:简单从句
NP:名词短语
VP:动词短语
PU:断句符,通常是句号、问号、感叹号等标点符号
LCP:方位词短语
PP:介词短语
CP:由‘的’构成的表示修饰性关系的短语
DNP:由‘的’构成的表示所属关系的短语
ADVP:副词短语
ADJP:形容词短语
DP:限定词短语
QP:量词短语
NN:常用名词
NR:固有名词
NT:时间名词
PN:代词
VV:动词
VC:是
CC:表示连词
VE:有
VA:表语形容词
AS:内容标记(如:了)
VRD:动补复合词
CD: 表示基数词
DT: determiner 表示限定词
EX: existential there 存在句
FW: foreign word 外来词
IN: preposition or conjunction, subordinating 介词或从属连词
JJ: adjective or numeral, ordinal 形容词或序数词
JJR: adjective, comparative 形容词比较级
JJS: adjective, superlative 形容词最高级
LS: list item marker 列表标识
MD: modal auxiliary 情态助动词
PDT: pre-determiner 前位限定词
POS: genitive marker 所有格标记
PRP: pronoun, personal 人称代词
RB: adverb 副词
RBR: adverb, comparative 副词比较级
RBS: adverb, superlative 副词最高级
RP: particle 小品词 
SYM: symbol 符号
TO:”to” as preposition or infinitive marker 作为介词或不定式标记 
WDT: WH-determiner WH限定词
WP: WH-pronoun WH代词
WP$: WH-pronoun, possessive WH所有格代词
WRB:Wh-adverb WH副词
 
关系表示
abbrev: abbreviation modifier,缩写
acomp: adjectival complement,形容词的补充;
advcl : adverbial clause modifier,状语从句修饰词
advmod: adverbial modifier状语
agent: agent,代理,一般有by的时候会出现这个
amod: adjectival modifier形容词
appos: appositional modifier,同位词
attr: attributive,属性
aux: auxiliary,非主要动词和助词,如BE,HAVE SHOULD/COULD等到
auxpass: passive auxiliary 被动词
cc: coordination,并列关系,一般取第一个词
ccomp: clausal complement从句补充
complm: complementizer,引导从句的词好重聚中的主要动词
conj : conjunct,连接两个并列的词。
cop: copula。系动词(如be,seem,appear等),(命题主词与谓词间的)连系
csubj : clausal subject,从主关系
csubjpass: clausal passive subject 主从被动关系
dep: dependent依赖关系
det: determiner决定词,如冠词等
dobj : direct object直接宾语
expl: expletive,主要是抓取there
infmod: infinitival modifier,动词不定式
iobj : indirect object,非直接宾语,也就是所以的间接宾语;
mark: marker,主要出现在有“that” or “whether”“because”, “when”,
mwe: multi-word expression,多个词的表示
neg: negation modifier否定词
nn: noun compound modifier名词组合形式
npadvmod: noun phrase as adverbial modifier名词作状语
nsubj : nominal subject,名词主语
nsubjpass: passive nominal subject,被动的名词主语
num: numeric modifier,数值修饰
number: element of compound number,组合数字
parataxis: parataxis: parataxis,并列关系
partmod: participial modifier动词形式的修饰
pcomp: prepositional complement,介词补充
pobj : object of a preposition,介词的宾语
poss: possession modifier,所有形式,所有格,所属
possessive: possessive modifier,这个表示所有者和那个’S的关系
preconj : preconjunct,常常是出现在 “either”, “both”, “neither”的情况下
predet: predeterminer,前缀决定,常常是表示所有
prep: prepositional modifier
prepc: prepositional clausal modifier
prt: phrasal verb particle,动词短语
punct: punctuation,这个很少见,但是保留下来了,结果当中不会出现这个
purpcl : purpose clause modifier,目的从句
quantmod: quantifier phrase modifier,数量短语
rcmod: relative clause modifier相关关系
ref : referent,指示物,指代
rel : relative
root: root,最重要的词,从它开始,根节点
tmod: temporal modifier
xcomp: open clausal complement
xsubj : controlling subject 掌控者
中心语为谓词
  subj — 主语
 nsubj — 名词性主语(nominal subject) (同步,建设)
   top — 主题(topic) (是,建筑)
npsubj — 被动型主语(nominal passive subject),专指由“被”引导的被动句中的主语,一般是谓词语义上的受事 (称作,镍)
 csubj — 从句主语(clausal subject),中文不存在
 xsubj — x主语,一般是一个主语下面含多个从句 (完善,有些)
中心语为谓词或介词   
   obj — 宾语
  dobj — 直接宾语 (颁布,文件)
  iobj — 间接宾语(indirect object),基本不存在
 range — 间接宾语为数量词,又称为与格 (成交,元)
  pobj — 介词宾语 (根据,要求)
  lobj — 时间介词 (来,近年)
中心语为谓词
  comp — 补语
 ccomp — 从句补语,一般由两个动词构成,中心语引导后一个动词所在的从句(IP) (出现,纳入)
 xcomp — x从句补语(xclausal complement),不存在   
 acomp — 形容词补语(adjectival complement)
 tcomp — 时间补语(temporal complement) (遇到,以前)
lccomp — 位置补语(localizer complement) (占,以上)
       — 结果补语(resultative complement)
中心语为名词
   mod — 修饰语(modifier)
  pass — 被动修饰(passive)
  tmod — 时间修饰(temporal modifier)
 rcmod — 关系从句修饰(relative clause modifier) (问题,遇到)
 numod — 数量修饰(numeric modifier) (规定,若干)
ornmod — 序数修饰(numeric modifier)
   clf — 类别修饰(classifier modifier) (文件,件)
  nmod — 复合名词修饰(noun compound modifier) (浦东,上海)
  amod — 形容词修饰(adjetive modifier) (情况,新)
advmod — 副词修饰(adverbial modifier) (做到,基本)
  vmod — 动词修饰(verb modifier,participle modifier)
prnmod — 插入词修饰(parenthetical modifier)
   neg — 不定修饰(negative modifier) (遇到,不)
   det — 限定词修饰(determiner modifier) (活动,这些)
 possm — 所属标记(possessive marker),NP
  poss — 所属修饰(possessive modifier),NP
  dvpm — DVP标记(dvp marker),DVP (简单,的)
dvpmod — DVP修饰(dvp modifier),DVP (采取,简单)
  assm — 关联标记(associative marker),DNP (开发,的)
assmod — 关联修饰(associative modifier),NP|QP (教训,特区)
  prep — 介词修饰(prepositional modifier) NP|VP|IP(采取,对)
 clmod — 从句修饰(clause modifier) (因为,开始)
 plmod — 介词性地点修饰(prepositional localizer modifier) (在,上)
   asp — 时态标词(aspect marker) (做到,了)
partmod– 分词修饰(participial modifier) 不存在
   etc — 等关系(etc) (办法,等)
中心语为实词
  conj — 联合(conjunct)
   cop — 系动(copula) 双指助动词????
    cc — 连接(coordination),指中心词与连词 (开发,与)
其它
  attr — 属性关系 (是,工程)
cordmod– 并列联合动词(coordinated verb compound) (颁布,实行)
  mmod — 情态动词(modal verb) (得到,能)
    ba — 把字关系
tclaus — 时间从句 (以后,积累)
       — semantic dependent
   cpm — 补语化成分(complementizer),一般指“的”引导的CP (振兴,的)
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Comments
  • The code in response.rs should define an enumeration to mark the error type

    The code in response.rs should define an enumeration to mark the error type

    use serde::{Deserialize, Serialize};
    
    #[derive(Clone, Debug, Deserialize, Serialize)]
    pub struct DataResponse<T> {
        pub code: i32,
        pub msg: String,
        pub data: T,
    }
    
    #[derive(Clone, Debug, Deserialize, Serialize)]
    pub struct SimpleResponse {
        pub code: i32,
        pub msg: String,
    }
    
    #[allow(dead_code)]
    pub struct CodeResponse {
        pub code: i32,
    }
    
    #[allow(dead_code)]
    impl <T> DataResponse<T> {
        pub fn new(code: i32, msg: String, data: T) -> DataResponse<T> {
            DataResponse {
                code,
                msg,
                data,
            }
        }
    }
    

    The code here should preferably be defined by enumeration, and the encapsulation method should be written as a custom method, which is more flexible

    enhancement 
    opened by Jzow 0
Owner
James Zow
slightly understand Java, love CV / NLP and Rust language
James Zow
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