├── README.md
├── aip                   //SDK目录
│   ├── __init__.py       //导出类
│   ├── base.py           //aip基类
│   ├── http.py           //http请求
│   └── nlp.py //自然语言处理
└── setup.py              //setuptools安装

from aip import AipNlp

""" 你的 APPID AK SK """
APP_ID = '你的 App ID'
API_KEY = '你的 Api Key'
SECRET_KEY = '你的 Secret Key'

client = AipNlp(APP_ID, API_KEY, SECRET_KEY)

text = "百度是一家高科技公司";
charset="UTF-8"

""" 调用词法分析 """
client.lexer(text);

{
  "status":0,
  "version":"ver_1_0_1",
  "results":[
    {
      "retcode":0,
      "text":"百度是一家高科技公司",
      "items":[
         {
           "byte_length":4,
           "byte_offset":0,
           "formal":"",
           "item":"百度",
           "ne":"ORG",
           "pos":"",
           "uri":"",
           "loc_details":[ ],
           "basic_words":["百度"]
         },
         {
           "byte_length":2,
           "byte_offset":4,
           "formal":"",
           "item":"是",
           "ne":"",
           "pos":"v",
           "uri":"",
           "loc_details":[ ],
           "basic_words":["是"]
         },
         {
           "byte_length":4,
           "byte_offset":6,
           "formal":"",
           "item":"一家",
           "ne":"",
           "pos":"m",
           "uri":"",
           "loc_details":[ ],
           "basic_words":["一","家"]
         },
         {
           "byte_length":6,
           "byte_offset":10,
           "formal":"",
           "item":"高科技",
           "ne":"",
           "pos":"n",
           "uri":"",
           "loc_details":[ ],
           "basic_words":["高","科技"]
         },
         {
           "byte_length":4,
           "byte_offset":16,
           "formal":"",
           "item":"公司",
           "ne":"",
           "pos":"n",
           "uri":"",
           "loc_details":[ ],
           "basic_words":["公司"]
         }
      ]
    }
  ]
}

text = "百度是一家高科技公司"

""" 调用词法分析（定制版） """
client.lexerCustom(text);

参考词法分析接口

text = "张飞"

""" 调用依存句法分析 """
client.depParser(text);

""" 如果有可选参数 """
options = {}
options["mode"] = 1

""" 带参数调用依存句法分析 """
client.depParser(text, options)

{
    "log_id": 12345,
    "text":"今天天气怎么样",
    "items":[
        {
            "id":"1", //id
            "word":"今天", //word
            "postag":"t", //POS tag
            "head":"2", //id of current word's parent
            "deprel":"ATT"  //depend relations between current word and parent
        },
        {
            "id":"2",
            "word":"天气",
            "postag":"n",
            "head":"3",
            "deprel":"SBV",
        },
        {
            "id":"3",
            "word":"怎么样",
            "postag":"r",
            "head":"0",
            "deprel":"HED",
        }
    ]
}

word = "张飞"

""" 调用词向量表示 """
client.wordEmbedding(word);

{
  "word": "张飞",
  "vec": [
    0.233962,
    0.336867,
    0.187044,
    0.565261,
    0.191568,
    0.450725,
    ...
    0.43869,
    -0.448038,
    0.283711,
    -0.233656,
    0.555556
  ]
}

text = "床前明月光"

""" 调用DNN语言模型 """
client.dnnlm(text);

{
  "text": "床前明月光",
  "items": [
    {
      "word": "床",
      "prob": 0.0000385273
    },
    {
      "word": "前",
      "prob": 0.0289018
    },
    {
      "word": "明月",
      "prob": 0.0284406
    },
    {
      "word": "光",
      "prob": 0.808029
    }
  ],
  "ppl": 79.0651
}

word1 = "北京"

word2 = "上海"

""" 调用词义相似度 """
client.wordSimEmbedding(word1, word2);

""" 如果有可选参数 """
options = {}
options["mode"] = 0

""" 带参数调用词义相似度 """
client.wordSimEmbedding(word1, word2, options)

{
    "score": 0.456862,
    "words": {
      "word_1": "北京",
      "word_2": "上海"
    }
}

text1 = "浙富股份"

text2 = "万事通自考网"

""" 调用短文本相似度 """
client.simnet(text1, text2);

""" 如果有可选参数 """
options = {}
options["model"] = "CNN"

""" 带参数调用短文本相似度 """
client.simnet(text1, text2, options)

{
    "log_id": 12345,
    "texts":{
        "text_1":"浙富股份",
        "text_2":"万事通自考网"
    },
    "score":0.3300237655639648 //相似度结果
},

text = "三星电脑电池不给力"

""" 调用评论观点抽取 """
client.commentTag(text);

""" 如果有可选参数 """
options = {}
options["type"] = 13

""" 带参数调用评论观点抽取 """
client.commentTag(text, options)

{
    "items": [
        {
        "prop":"电池",
        "adj": "不给力",
        "sentiment": 0,
        "begin_pos": 8,
        "end_pos": 18,
        "abstract":"三星电脑<span>电池不给力</span>"
        }
    ]
}

text = "苹果是一家伟大的公司"

""" 调用情感倾向分析 """
client.sentimentClassify(text);

{
    "text":"苹果是一家伟大的公司",
    "items":[
        {
            "sentiment":2,    //表示情感极性分类结果
            "confidence":0.40, //表示分类的置信度
            "positive_prob":0.73, //表示属于积极类别的概率
            "negative_prob":0.27  //表示属于消极类别的概率
        }
    ]
}

title = "iphone手机出现“白苹果”原因及解决办法，用苹果手机的可以看下"

content = "如果下面的方法还是没有解决你的问题建议来我们门店看下成都市锦江区红星路三段99号银石广场24层01室。"

""" 调用文章标签 """
client.keyword(title, content);

{
    "log_id": 4457308639853058292,
    "items": [
        {
            "score": 0.997762,
            "tag": "iphone"
        },
        {
            "score": 0.861775,
            "tag": "手机"
        },
        {
            "score": 0.845657,
            "tag": "苹果"
        },
        {
            "score": 0.83649,
            "tag": "苹果公司"
        },
        {
            "score": 0.797243,
            "tag": "数码"
        }
    ]
}

title = "欧洲冠军杯足球赛"

content = "欧洲冠军联赛是欧洲足球协会联盟主办的年度足球比赛，代表欧洲俱乐部足球最高荣誉和水平，被认为是全世界最高素质、最具影响力以及最高水平的俱乐部赛事，亦是世界上奖金最高的足球赛事和体育赛事之一。"

""" 调用文章分类 """
client.topic(title, content);

{
    "log_id": 5710764909216517248,
    "item": {
        "lv2_tag_list": [
            {
                "score": 0.895467,
                "tag": "足球"
            },
            {
                "score": 0.794878,
                "tag": "国际足球"
            }
        ],
        "lv1_tag_list": [
            {
                "score": 0.88808,
                "tag": "体育"
            }
        ]
    }
}

text = "百度是一家人工只能公司"

""" 调用文本纠错 """
client.ecnet(text);

{
    "log_id": 6770395607901559829,
    "item": {
        "vec_fragment": [
            {
                "ori_frag": "只能",
                "begin_pos": 21,
                "correct_frag": "智能",
                "end_pos": 27
            }
        ],
        "score": 0.875169,
        "correct_query": "百度是一家人工智能公司"
    },
    "text": "百度是一家人工只能公司"
}

text = "本来今天高高兴兴"

""" 调用对话情绪识别接口 """
client.emotion(text);

""" 如果有可选参数 """
options = {}
options["scene"] = "talk"

""" 带参数调用对话情绪识别接口 """
client.emotion(text, options)

{
    "log_id": 4258005459150262970,
    "text": "本来今天高高兴兴",
    "items": [
        {
            "prob": 0.998619,
            "label": "neutral"
        },
        {
            "prob": 0.00138141,
            "label": "pessimistic"
        },
    ]
}

content = "麻省理工学院的研究团队为无人机在仓库中使用RFID技术进行库存查找等工作，创造了一种..."

maxSummaryLen = 300

""" 调用新闻摘要接口 """
client.newsSummary(content, maxSummaryLen);

""" 如果有可选参数 """
options = {}
options["title"] = "标题"

""" 带参数调用新闻摘要接口 """
client.newsSummary(content, maxSummaryLen, options)

{
    "log_id": 4258005459150262970,
    "summary": "麻省理工学院的研究团队为无人机在仓库中使用RFID技术进行库存查找等工作，创造了一种聪明的新方式。使用RFID标签更换仓库中的条形码，将帮助提升自动化并提高库存管理的准确性。几家公司已经解决了无人机读取RFID的技术问题。麻省理工学院的新解决方案，名为Rfly，允许无人机阅读RFID标签，而不用捆绑巨型读卡器。无人机接收从远程RFID读取器发送的信号，然后转发它读取附近的标签。"
}

text = "上海市浦东新区纳贤路701号百度上海研发中心 F4A000 张三"

""" 调用地址识别接口 """
client.address(text);

{
    "province": "上海市",
    "city": "上海市",
    "province_code": "310000",
    "log_id": 1420038644986026316,
    "text": "上海市浦东新区纳贤路701号百度上海研发中心 F4A000 张三",
    "town": "张江镇",
    "phonenum": "",
    "detail": "纳贤路701号百度上海研发中心F4A000",
    "county": "浦东新区",
    "person": "张三",
    "town_code": "310115125",
    "county_code": "310115",
    "city_code": "310100"
}