0.1.0 • Published 7 years ago

bosonnlp v0.1.0

Weekly downloads
4
License
ISC
Repository
github
Last release
7 years ago

BosonNLP

BosonNLP is a node sdk for http://bosonnlp.com .

Build Status npm NPM Downloads

Installation

$ npm install bosonnlp

Usage

var bosonnlp = require('bosonnlp');
var nlp = new bosonnlp.BosonNLP('YOUR_API_KEY');
nlp.ner('成都商报记者 姚永忠', function (result) {
	console.log(result);
});
//[{"tag": ["ns", "n", "n", "nr"], 
//  "word": ["成都", "商报", "记者", "姚永忠"], 
//  "entity": [[0, 2, "product_name"], [3, 4, "person_name"]]}]

API

  • tag(content, callback) - Tokenization and part of speech tagging.
  • ner(content, callback) - Named-entity recognition.
  • extractKeywords(content, callback) - Tokenization and compute word weight.
  • sentiment(content, callback) - Automatic detection of opinions embodied in text.
  • depparser(content, callback) - Work out the grammatical structure of sentences
  • classify(content, callback) - categorization the given articles.
  • suggest(term, callback) - Get relative words.

tag

POS Tagging DOC

var bosonnlp = require('bosonnlp');
var nlp = new bosonnlp.BosonNLP('YOUR_API_KEY');

var text = "这个世界好复杂";
boson.tag(text, function (data) {
	console.log(data);
});
// [{"tag": ["r", "n", "d", "a"], 
// "word": ["这个", "世界", "好", "复杂"]}]

var text = ['这个世界好复杂', '计算机是科学么'];
boson.tag(text, function (data) {
	data = JSON.parse(data); 

	// ["r", "n", "d", "a"]
	console.log(data[0].tag); 

	// ["这个", "世界", "好", "复杂"]
	console.log(data[0].word); 

	// ["n", "vshi", "n", "y"]
	console.log(data[1].tag); 

	// ["计算机", "是", "科学", "么"]
	console.log(data[1].word); 
});

ner

var bosonnlp = require('bosonnlp');
var nlp = new bosonnlp.BosonNLP('YOUR_API_KEY');
nlp.ner('成都商报记者 姚永忠', function (result) {
	console.log(result);
});
//[{"tag": ["ns", "n", "n", "nr"], 
//  "word": ["成都", "商报", "记者", "姚永忠"], 
//  "entity": [[0, 2, "product_name"], [3, 4, "person_name"]]}]

var content = ["对于该小孩是不是郑尚金的孩子,目前已做亲子鉴定,结果还没出来,",
                "纪检部门仍在调查之中。成都商报记者 姚永忠"];
nlp.ner(content, function (result) {
	console.log(result);
});
//[{"tag": ["p","r","n","vshi","d","vshi","nr","ude","n","wd","t","d","v","n","n","wd","n","d","d","v","wd"],
//  "word": ["对于","该","小孩","是","不","是","郑尚金","的","孩子",",","目前","已","做","亲子","鉴定",",",
// "结果","还","没","出来",","], 
//  "entity": [[6, 7, "person_name"]]},
//  {"tag": ["n","n","d","p","v","f","wj","ns","n","n","nr"], 
//  "word": ["纪检","部门","仍","在","调查","之中","。","成都","商报","记者","姚永忠"], 
//  "entity": [[7,9,"product_name"],[9,10,"job_title"],[10,11,"person_name"]]}]

extractKeywords

var bosonnlp = require('bosonnlp');
var nlp = new bosonnlp.BosonNLP('YOUR_API_KEY');
var text = ["病毒式媒体网站:让新闻迅速蔓延"];
nlp.extractKeywords(text, function (data) {
	data = JSON.parse(data);
	console.log(data);
});

sentiment

var text = ['他是个傻逼','美好的世界'];
boson.sentiment(text, function (data) {
    // [非负面概率, 负面概率]
    // [[0.6519134382562579, 0.34808656174374203], [0.92706110187413, 0.07293889812586994]]
	console.log(data);
});

depparser

Depparser Doc

名称解释举例
ROOT核心词警察打击犯罪。
SBJ主语成分警察打击犯罪。
OBJ宾语成分警察打击犯罪
PU标点符号你好!
TMP时间成分昨天下午下雨了。
LOC位置成分在北京开会。
MNR方式成分以最快的速度冲向了终点。
POBJ介宾成分对客人很热情。
PMOD介词修饰这个产品到今天才完成。
NMOD名词修饰这是一个错误。
VMOD动词修饰狠狠地他。
VRD动结式(第二动词为第一动词结果) 福建省涌现出大批人才。
DEG连接词“的”结构 的妈妈是超人。
DEV“地”结构狠狠地看我一眼。
LC位置词结构我在书房里吃饭。
M量词结构我有只小猪。
AMOD副词修饰一批中企业折戟上海。
PRN括号成分北京(首都)很大。
VC动词“是”修饰 我把你看做是妹妹。
COOR并列关系希望能贯彻 执行该方针
CS从属连词成分如果可行,我们进行推广。
DEC关系从句“的” 这是以前不曾遇到过的情况。
var text = ['我以最快的速度吃了午饭']
boson.depparser(text, function (data) {
	console.log("depparser:", data);
});

classify

Classes Doc

编号分类
0体育
1教育
2财经
3社会
4娱乐
5军事
6国内
7科技
8互联网
9房产
10国际
11女人
12汽车
13游戏
var text = ['俄否决安理会谴责叙军战机空袭阿勒颇平民',
			'邓紫棋谈男友林宥嘉:我觉得我比他唱得好',
			'Facebook收购印度初创公司'];
boson.classify(text, function (data) {
    // [5, 4, 8]
	console.log("classify:", data);
	test.done();
});

suggest

var term = '粉丝';
boson.suggest(term, function (data) {
	console.log("suggest:", data);
});

var options = {};
// options.top_k default 10
options.top_k = 2;
boson.suggest(term, options, function (data) {
	console.log("suggest:", data);
});
0.1.0

7 years ago

0.0.9

10 years ago

0.0.8

10 years ago

0.0.7

10 years ago

0.0.6

10 years ago

0.0.5

10 years ago

0.0.4

10 years ago

0.0.3

10 years ago

0.0.2

10 years ago

0.0.1

10 years ago