Example #1
0
 /// <summary>
 /// 页面加载
 /// </summary>
 /// <param name="sender"></param>
 /// <param name="e"></param>
 private void Form1_Load(object sender, EventArgs e)
 {
     //初始化客户端
     client = new Nlp(PartOfSpeech.API_KEY, PartOfSpeech.SECRET_KEY);
     //设置超时时间
     client.Timeout = 60000;
 }
        public static void SimNet()
        {
            var nlp    = new Nlp("Api Key", "Secret Key");
            var result = nlp.Simnet("你好百度", "你好世界");

            Console.Write(result);
        }
        public void WordSeg()
        {
            Nlp nlp    = new Nlp("Api Key", "Secret Key");
            var result = nlp.WordSeg("今天天气不错");

            Console.Write(result);
        }
Example #4
0
        // 短文本相似度
        // 短文本相似度接口用来判断两个文本的相似度得分
        public static SimnetSerialize SimnetDemo(string text1, string text2)
        {
            //"浙富股份","万事通自考网"

            client = new Nlp(API_KEY, SECRET_KEY);

            // 调用短文本相似度,可能会抛出网络等异常,请使用try/catch捕获
            var result = client.Simnet(text1, text2);
            //System.Diagnostics.Debug.WriteLine(result);

            /*
             * // 如果有可选参数
             * var options = new Dictionary<string, object>{
             *  {"model", "CNN"}
             * };
             *
             * // 带参数调用短文本相似度
             * result = client.Simnet(text1, text2, options);
             * //System.Diagnostics.Debug.WriteLine(result);
             */

            SimnetSerialize res = new SimnetSerialize();

            res.score = result["score"].ToString();
            return(res);
        }
Example #5
0
        // 词法分析
        public static ResultSerialize LexerDemo(string text)
        {
            client = new Nlp(API_KEY, SECRET_KEY);

            JObject result = client.Lexer(text);
            //System.Diagnostics.Debug.WriteLine(result);

            ResultSerialize res = new ResultSerialize();

            res.log_id = result["log_id"].ToString();
            res.text   = result["text"].ToString();
            res.items  = (JArray)result["items"];
            for (int i = 0; i < res.items.Count; i++)
            {
                LexerSerialize _item = new LexerSerialize();
                //loc_details =
                _item.byte_offset = int.Parse(res.items[i]["byte_offset"].ToString());
                _item.uri         = res.items[i]["uri"].ToString();
                _item.pos         = res.items[i]["pos"].ToString();
                _item.ne          = res.items[i]["ne"].ToString();
                _item.item        = res.items[i]["item"].ToString();
                //basic_words =
                _item.byte_length = int.Parse(res.items[i]["byte_length"].ToString());
                _item.formal      = res.items[i]["formal"].ToString();
                //System.Diagnostics.Debug.Print("item是 " + res.items[i]["item"].ToString());
            }
            return(res);
        }
        public static void WordPos()
        {
            var nlp    = new Nlp("Api Key", "Secret Key");
            var result = nlp.WordPos("今天天气不错");

            Console.Write(result);
        }
Example #7
0
File: NlpDemo.cs Project: ly2018/t
        public void WordSeg()
        {
            Nlp nlp    = new Nlp(AiKeySecret.ApiKey, AiKeySecret.SecretKey);
            var result = nlp.WordSeg("今天天气不错");

            Console.Write(result);
        }
        public static void Dnnlm()
        {
            var nlp    = new Nlp("Api Key", "Secret Key");
            var result = nlp.DNN_LM_Cn("今天天气不错");

            Console.Write(result);
        }
Example #9
0
        // 词义相似度
        // 两个文本相似度得分
        public static WordSimEmbeddingSerialize WordSimEmbeddingDemo(string word1, string word2, Dictionary <string, object> options = null)
        {
            //"北京", "上海"
            client = new Nlp(API_KEY, SECRET_KEY);

            // 调用词义相似度,可能会抛出网络等异常,请使用try/catch捕获
            var result = client.WordSimEmbedding(word1, word2);
            //System.Diagnostics.Debug.WriteLine(result);

            // 如果有可选参数

            /*
             * var options = new Dictionary<string, object>{
             *  {"mode", 0}
             * };
             */

            // 带参数调用词义相似度
            //var result = client.WordSimEmbedding(word1, word2, options);
            //System.Diagnostics.Debug.WriteLine(result);

            WordSimEmbeddingSerialize res = new WordSimEmbeddingSerialize();

            res.score = result["score"].ToString();
            return(res);
        }
Example #10
0
        /// <summary>
        /// 文本纠错,主要程序
        /// </summary>
        /// <param name="path">excel试题库</param>
        /// <param name="resultpath">错误内容存放的位置</param>
        public static void Ecnet(string path, string resultpath, out int count)
        {
            count = 0;
            DataTable       dt          = util.ExcelToDataTable(path, true);
            List <Question> list        = util.DatatableConvertToQuestion(dt);
            Nlp             client      = util.CreateClient();
            StringBuilder   sb          = null;
            StreamWriter    errorprint  = new StreamWriter("error.txt", true);
            StreamWriter    ResultPrint = new StreamWriter(resultpath, true);

            foreach (Question question in list)
            {
                sb = new StringBuilder(question.Title.Replace("_______", util.GetAnswerStr(question, question.Answer)));
                sb.Append(question.Choosea);
                sb.Append(question.Chooseb);
                sb.Append(question.Choosec);
                sb.Append(question.Choosed);
                sb.Append(question.Explain);
                int i = 0;
                while (sb.Length - 255 * i > 0)
                {
                    try
                    {
                        JObject result = client.Ecnet(sb.Length - 255 * i > 255 ? sb.ToString().Substring(i * 255, 255) : sb.ToString().Substring(i * 255, sb.Length - 255 * i));
                        JToken  error_code;
                        //如果发生错误进行记录
                        if (result.TryGetValue("error_code", out error_code))
                        {
                            string ErrorPrint = question.AllID + "||" + error_code.ToString() + "||" + result["error_msg"];
                            errorprint.WriteLine(ErrorPrint);
                            errorprint.Flush();
                            Thread.Sleep(500);
                            break;
                        }
                        else //如果获得正确结果的处理
                        {
                            decimal score        = Convert.ToDecimal(result["item"]["score"]);
                            string  CorrectPrint = "Count:" + count + "||path:" + path + "||ALLID:" + question.AllID + "||SNID:" + question.SNID + "||log_id:" + result["log_id"];
                            Console.WriteLine(CorrectPrint);
                            //如果有需要纠错的内容
                            if (score != 0)
                            {
                                ResultPrint.WriteLine(CorrectPrint + result.ToString());
                                ResultPrint.Flush();
                                Console.WriteLine(result);
                            }
                        }
                        i++;
                        Thread.Sleep(500);
                    }
                    catch (Exception e)
                    {
                        errorprint.WriteLine(e.Message); errorprint.Flush();
                    }
                }
                count++;
            }
            errorprint.Close();
            ResultPrint.Close();
        }
Example #11
0
File: NlpDemo.cs Project: ly2018/t
        public static void WordPos()
        {
            var nlp    = new Nlp(AiKeySecret.ApiKey, AiKeySecret.SecretKey);
            var result = nlp.WordPos("今天天气不错");

            Console.Write(result);
        }
        public void Lexer()
        {
            Nlp nlp    = new Nlp("Api Key", "Secret Key");
            var result = nlp.Lexer("今天天气不错");

            Console.Write(result);
        }
        public static void CommentTag()
        {
            var nlp    = new Nlp("Api Key", "Secret Key");
            var result = nlp.CommentTag("个人觉得这车不错,外观漂亮年轻,动力和操控性都不错", 10);

            Console.Write(result);
        }
        public static void SentimentClassify()
        {
            var nlp    = new Nlp("Api Key", "Secret Key");
            var result = nlp.SentimentClassify("个人觉得这车不错,外观漂亮年轻,动力和操控性都不错");

            Console.Write(result);
        }
Example #15
0
        // 依存句法分析
        public static ResultSerialize DepParserDemo(string text)
        {
            client = new Nlp(API_KEY, SECRET_KEY);

            // 调用依存句法分析,可能会抛出网络等异常,请使用try/catch捕获
            JObject result = client.DepParser(text);
            //System.Diagnostics.Debug.WriteLine(result);

            ResultSerialize res = new ResultSerialize();

            res.log_id = result["log_id"].ToString();
            res.text   = result["text"].ToString();
            res.items  = (JArray)result["items"];
            for (int i = 0; i < res.items.Count; i++)
            {
                DepParserSerialize _gammar = new DepParserSerialize();
                //_gammar.id = res.items[i]["id"].ToString();
                _gammar.word   = res.items[i]["word"].ToString();
                _gammar.postag = res.items[i]["postag"].ToString();
                _gammar.head   = res.items[i]["head"].ToString();
                _gammar.deprel = res.items[i]["deprel"].ToString();
            }
            return(res);

            // 如果有可选参数
            var options = new Dictionary <string, object> {
                { "mode", 1 }
            };

            // 带参数调用依存句法分析
            result = client.DepParser(text, options);
        }
Example #16
0
 public Form1()
 {
     InitializeComponent();
     txtRecorder = new TxtRecorder();
     GetSettings();
     nlp           = new Nlp(txtAppKey.Text, txtSecretKey.Text);
     imageClassify = new ImageClassify(txtAppKey.Text, txtSecretKey.Text);
 }
Example #17
0
        private Nlp CreateLanguageClient()
        {
            var apiKey    = string.IsNullOrEmpty(App.CloudKey) ? App.ApiKey : App.CloudKey;
            var secretKey = string.IsNullOrEmpty(App.CloudSecret) ? App.SecretKey : App.CloudSecret;
            var client    = new Nlp(apiKey, secretKey);

            client.Timeout = 60000;
            return(client);
        }
 private Nlp getClient()
 {
     if (this.nlp != null)
     {
         return(this.nlp);
     }
     this.nlp         = new Nlp(APIKEY, SECRETKEY);
     this.nlp.Timeout = 60000;
     return(this.nlp);
 }
        public static void WordEmbedding()
        {
            var nlp = new Nlp("Api Key", "Secret Key");
            // 词相似度
            var result = nlp.WordEmbeddingSimilarity("北京", "上海");

            Console.Write(result);
            // 词向量
            result = nlp.WordEmbeddingVector("北京");
            Console.Write(result);
        }
Example #20
0
File: NlpDemo.cs Project: ly2018/t
        public static void DepParser()
        {
            var nlp     = new Nlp(AiKeySecret.ApiKey, AiKeySecret.SecretKey);
            var options = new Dictionary <string, object>()
            {
                { "mode", 1 }
            };
            var result = nlp.DepParser("今天天气不错", options);

            Console.Write(result);
        }
Example #21
0
        public async Task <string> AnalyseAsync(long id)
        {
            var news = await _context.News.FindAsync(id);

            if (news.Analyseresult == null)
            {
                Encoding.RegisterProvider(CodePagesEncodingProvider.Instance);
                StreamReader sr   = new StreamReader("AnalyzerConfigs/apiconfig.json");
                string       json = sr.ReadToEnd();
                //json文件转为 对象  T 创建的类 字段名 应该和json文件中的保持一致
                var data = JsonConvert.DeserializeObject <T>(json);

                var APP_ID     = data.appid;
                var API_KEY    = data.apikey;
                var SECRET_KEY = data.secretkey;

                var client = new Nlp(API_KEY, SECRET_KEY);
                client.Timeout = 60000;  // 修改超时时间

                string src = news.Content;
                if (news.Content.Length > 1020)
                {
                    if (news.Content.Length > 2980)
                    {
                        _ = src.Substring(0, 2980);
                    }
                    var content = src;

                    var maxSummaryLen = 300;
                    // 如果有可选参数
                    var options = new Dictionary <string, object> {
                        { "title", news.Title }
                    };
                    // 带参数调用新闻摘要接口
                    var ress = client.NewsSummary(content, maxSummaryLen, options);
                    Console.WriteLine(ress);
                    src = (string)ress["summary"];
                }
                var      result = client.SentimentClassify(src);
                String[] res    = { "negative", "neutral", "positive" };
                if (result.ContainsKey("error_code"))
                {
                    return(result.ToString());
                }
                news.Analyseresult         = res[(int)result["items"][0]["sentiment"]];
                _context.Entry(news).State = EntityState.Modified;
                await _context.SaveChangesAsync();

                return(res[(int)result["items"][0]["sentiment"]]);
            }
            return(news.Analyseresult);
        }
Example #22
0
 private void SaveSettings()
 {
     Settings.Default.AppKey        = txtAppKey.Text;
     Settings.Default.SecretKey     = txtSecretKey.Text;
     Settings.Default.ConnectString = txtConnectString.Text;
     Settings.Default.TextTable     = txtTable.Text;
     Settings.Default.TextColumn    = txtColumn.Text;
     Settings.Default.OverLoad      = txtOverLoad.Text;
     Settings.Default.TopicLimit    = txtTopicLimit.Text;
     Settings.Default.TestTik       = txtTestTik.Text;
     Settings.Default.Save();
     nlp = new Nlp(txtAppKey.Text, txtSecretKey.Text);
 }
Example #23
0
 public BaiduAi(ILogger <BaiduAi> logger, Orc orc, FaceRecognition faceRecognition, BodyAnalysis bodyAnalysis, Speech speech,
                ImageClassify imageClassify, ImageSearch imageSearch, ImageEffects imageEffects, Nlp nlp)
 {
     _logger          = logger;
     _orc             = orc;
     _faceRecognition = faceRecognition;
     _bodyAnalysis    = bodyAnalysis;
     _speech          = speech;
     _imageClassify   = imageClassify;
     _imageSearch     = imageSearch;
     _imageEffects    = imageEffects;
     _nlp             = nlp;
 }
Example #24
0
        // 词向量表示
        public static WordEmbeddingSerialize WordEmbeddingDemo(string text)
        {
            client = new Nlp(API_KEY, SECRET_KEY);

            // 调用词向量表示,可能会抛出网络等异常,请使用try/catch捕获
            JObject result = client.WordEmbedding(text);
            //System.Diagnostics.Debug.WriteLine(result);

            WordEmbeddingSerialize res = new WordEmbeddingSerialize();

            res.word = result["word"].ToString();
            res.vec  = (JArray)result["vec"];
            return(res);
        }
Example #25
0
        public static void SentimentClassify()
        {
            var          nlp           = new Nlp(AppKey, AppSecret);
            const string sqlStr        = "SELECT  id,accountid,t_mk,vm_id FROM  dbo.Sys_TaskDaily";
            var          taskDailyList = DapperHelper.Query <TaskDailyEntity>(sqlStr, null);

            foreach (
                var result in
                from item in taskDailyList
                where !string.IsNullOrWhiteSpace(item.T_mk)
                select nlp.SentimentClassify(item.T_mk))
            {
                SimpleLog.Instance.WriteLogForFile("用户反馈情感分析", JsonConvert.SerializeObject(result));
            }

            Console.WriteLine("执行完毕!");
        }
Example #26
0
        // 评论观点抽取
        public static CommentTagSerialize CommentTagDemo(string text, Dictionary <string, object> options = null)
        {
            //"三星电脑电池不给力"

            client = new Nlp(API_KEY, SECRET_KEY);

            // 调用评论观点抽取,可能会抛出网络等异常,请使用try/catch捕获
            JObject result = null;

            try
            {
                //result = client.CommentTag(text); //不传option会报错
                //System.Diagnostics.Debug.WriteLine(result);

                // 如果有可选参数
                //var options = new Dictionary<string, object>{{"type", 13}};

                // 带参数调用评论观点抽取
                result = client.CommentTag(text, options);
            }
            catch
            {
                //System.Console.WriteLine("是否存在 ==>> " + (result != null));
                //System.Console.WriteLine("result子物体 ==>> " + result.Count);
            }

            CommentTagSerialize res = new CommentTagSerialize();

            res.items = (JArray)result["items"];
            //System.Console.WriteLine("res子物体");
            //System.Console.WriteLine("finally ==>> " + res.items.First.First);

            for (int i = 0; i < res.items.Count; i++)
            {
                CommentTag_Sub _item = new CommentTag_Sub();
                _item.prop      = res.items[i]["prop"].ToString();
                _item.adj       = res.items[i]["adj"].ToString();
                _item.sentiment = int.Parse(res.items[i]["sentiment"].ToString());
                _item.begin_pos = int.Parse(res.items[i]["begin_pos"].ToString());
                _item.end_pos   = int.Parse(res.items[i]["end_pos"].ToString());
                _item.abs       = res.items[i]["abstract"].ToString();
            }
            return(res);
        }
Example #27
0
        protected override void Init()
        {
            base.Init();
            nlpData = new Dictionary <WordType, string>();
            client  = new Nlp(API_KEY, SECRET_KEY)
            {
                Timeout = 60000
            };
            recognizer = new DictationRecognizer
            {
                AutoSilenceTimeoutSeconds = 1
            };

            recognizer.DictationComplete   += DictationComplete;
            recognizer.DictationResult     += DictationResult;
            recognizer.DictationError      += DictationError;
            recognizer.DictationHypothesis += DictationHypothesis;
            recognizer.Start();
        }
Example #28
0
        /// <summary>
        /// 分词
        /// </summary>
        public static void Nlp(string text)
        {
            try
            {
                if (nlp == null)
                {
                    string APP_ID     = "14902717";
                    string API_KEY    = "1qHCYskEsmQyMYYwGa2b4RI9";
                    string SECRET_KEY = "34uRO8hYapy7OTKGzuEDK3EeLyDsZOMt";
                    nlp         = new Nlp(API_KEY, SECRET_KEY);
                    nlp.Timeout = 60000;  // 修改超时时间
                }

                // 调用词法分析,可能会抛出网络等异常,请使用try/catch捕获
                var      result    = nlp.Lexer(text);
                BaiduNlp nlpEntity = JsonConvert.DeserializeObject <BaiduNlp>(result.ToString());
            }
            catch (Exception)
            {
                throw;
            }
        }
Example #29
0
        // DNN语言模型
        public static DnnlmCnSerialize DnnlmCnDemo(string text)
        {
            //床前明月光;
            client = new Nlp(API_KEY, SECRET_KEY);

            // 调用DNN语言模型,可能会抛出网络等异常,请使用try/catch捕获
            JObject result = client.DnnlmCn(text);
            //System.Diagnostics.Debug.WriteLine(result);

            DnnlmCnSerialize res = new DnnlmCnSerialize();

            res.log_id = result["log_id"].ToString();
            res.text   = result["text"].ToString();
            res.items  = (JArray)result["items"];
            for (int i = 0; i < res.items.Count; i++)
            {
                DnnlmCn_Sub _item = new DnnlmCn_Sub();
                _item.word = res.items[i]["word"].ToString();
                _item.prob = double.Parse(res.items[i]["prob"].ToString());
            }
            res.ppl = double.Parse(result["ppl"].ToString());
            return(res);
        }
Example #30
0
        public void Test()
        {
            nlp         = new Nlp("GksxjvtX80wjcSN660ZW8QZu", "ccTZ6jSyR2OMukV9CIPukoHy27GjGzVy");
            nlp.Timeout = 3000;

            bool shouldContinue = true;

            while (shouldContinue)
            {
                Console.WriteLine(" 开始英译中测试\n 输入esc退出测试 输入\'?\'百度翻译\n");
                while (TestEng2ChiSingle())
                {
                    ;
                }

                wordList.Reset();

                Console.WriteLine(" 开始中译英测试\n 输入esc退出测试 输入\'?\'百度翻译\n");
                while (TestChi2EngSingle())
                {
                    ;
                }

                if (shouldMarkAsPass && testPassFlag.Key && testPassFlag.Value)
                {
                    MarkAsPass();
                }

                shouldContinue = chooseContinue();

                if (shouldContinue)
                {
                    wordList.Reset();
                }
            }
        }