學習筆記CB011:lucene搜索引擎庫、IKAnalyzer中文切詞工具、檢索服務、查詢索引、導流、word2vec

時間 2019-11-15

標籤學習筆記 cb011 lucene 搜索引擎 ikanalyzer 中文工具檢索服務查詢索引導流 word2vec word vec 欄目搜索引擎简体版

原文原文鏈接

影視劇字幕聊天語料庫特色，把影視劇說話內容一句一句以回車換行羅列三千多萬條中國話，相鄰第二句極可能是第一句最好回答。一個問句有不少種回答，能夠根據相關程度以及歷史聊天記錄全部回答排序，找到最優，是一個搜索排序過程。javascript

lucene+ik。lucene開源免費搜索引擎庫，java語言開發。ik IKAnalyzer，開源中文切詞工具。語料庫切詞建索引，文本搜索作文本相關性檢索，把下一句取出做答案候選集，答案排序，問題分析。css

建索引。eclipse建立maven工程，maven自動生成pom.xml文件，配置包依賴信息，dependencies標籤中添加依賴：html

<dependency>
    <groupId>org.apache.lucene</groupId>
    <artifactId>lucene-core</artifactId>
    <version>4.10.4</version>
</dependency>
<dependency>
    <groupId>org.apache.lucene</groupId>
    <artifactId>lucene-queryparser</artifactId>
    <version>4.10.4</version>
</dependency>
<dependency>
    <groupId>org.apache.lucene</groupId>
    <artifactId>lucene-analyzers-common</artifactId>
    <version>4.10.4</version>
</dependency>
<dependency>
    <groupId>io.netty</groupId>
    <artifactId>netty-all</artifactId>
    <version>5.0.0.Alpha2</version>
</dependency>
<dependency>
    <groupId>com.alibaba</groupId>
    <artifactId>fastjson</artifactId>
    <version>1.1.41</version>
</dependency>

project標籤增長配置，依賴jar包自動拷貝lib目錄：java

<build>
  <plugins>
    <plugin>
      <groupId>org.apache.maven.plugins</groupId>
      <artifactId>maven-dependency-plugin</artifactId>
      <executions>
        <execution>
          <id>copy-dependencies</id>
          <phase>prepare-package</phase>
          <goals>
            <goal>copy-dependencies</goal>
          </goals>
          <configuration>
            <outputDirectory>${project.build.directory}/lib</outputDirectory>
            <overWriteReleases>false</overWriteReleases>
            <overWriteSnapshots>false</overWriteSnapshots>
            <overWriteIfNewer>true</overWriteIfNewer>
          </configuration>
        </execution>
      </executions>
    </plugin>
    <plugin>
      <groupId>org.apache.maven.plugins</groupId>
      <artifactId>maven-jar-plugin</artifactId>
      <configuration>
        <archive>
          <manifest>
            <addClasspath>true</addClasspath>
            <classpathPrefix>lib/</classpathPrefix>
            <mainClass>theMainClass</mainClass>
          </manifest>
        </archive>
      </configuration>
    </plugin>
  </plugins>
</build>

https://storage.googleapis.com/google-code-archive-downloads/v2/code.google.com/ik-analyzer/IK%20Analyzer%202012FF_hf1_source.rar 下載ik源代碼把src/org目錄拷到chatbotv1工程src/main/java下，刷新maven工程。python

com.shareditor.chatbotv1包下maven自動生成App.java，改爲Indexer.java：git

Analyzer analyzer = new IKAnalyzer(true);
IndexWriterConfig iwc = new IndexWriterConfig(Version.LUCENE_4_9, analyzer);
iwc.setOpenMode(OpenMode.CREATE);
iwc.setUseCompoundFile(true);
IndexWriter indexWriter = new IndexWriter(FSDirectory.open(new File(indexPath)), iwc);

BufferedReader br = new BufferedReader(new InputStreamReader(
        new FileInputStream(corpusPath), "UTF-8"));
String line = "";
String last = "";
long lineNum = 0;
while ((line = br.readLine()) != null) {
	line = line.trim();

	if (0 == line.length()) {
		continue;
	}

	if (!last.equals("")) {
		Document doc = new Document();
		doc.add(new TextField("question", last, Store.YES));
		doc.add(new StoredField("answer", line));
		indexWriter.addDocument(doc);
	}
	last = line;
	lineNum++;
	if (lineNum % 100000 == 0) {
		System.out.println("add doc " + lineNum);
	}
}
br.close();

indexWriter.forceMerge(1);
indexWriter.close();

編譯拷貝src/main/resources全部文件到target目錄，target目錄執行github

java -cp $CLASSPATH:./lib/:./chatbotv1-0.0.1-SNAPSHOT.jar com.shareditor.chatbotv1.Indexer ../../subtitle/raw_subtitles/subtitle.corpus ./index

生成索引目錄index經過lukeall-4.9.0.jar查看。web

檢索服務。netty建立http服務server，代碼在https://github.com/warmheartli/ChatBotCourse的chatbotv1目錄：算法

Analyzer analyzer = new IKAnalyzer(true);
QueryParser qp = new QueryParser(Version.LUCENE_4_9, "question", analyzer);
if (topDocs.totalHits == 0) {
	qp.setDefaultOperator(Operator.AND);
	query = qp.parse(q);
	System.out.println(query.toString());
	indexSearcher.search(query, collector);
	topDocs = collector.topDocs();
}

if (topDocs.totalHits == 0) {
	qp.setDefaultOperator(Operator.OR);
	query = qp.parse(q);
	System.out.println(query.toString());
	indexSearcher.search(query, collector);
	topDocs = collector.topDocs();
}

ret.put("total", topDocs.totalHits);
ret.put("q", q);
JSONArray result = new JSONArray();
for (ScoreDoc d : topDocs.scoreDocs) {
	Document doc = indexSearcher.doc(d.doc);
	String question = doc.get("question");
	String answer = doc.get("answer");
	JSONObject item = new JSONObject();
	item.put("question", question);
	item.put("answer", answer);
	item.put("score", d.score);
	item.put("doc", d.doc);
	result.add(item);
}
ret.put("result", result);

查詢索引，query詞作切詞拼lucene query，檢索索引question字段，匹配返回answer字段值做候選集，挑出候選集一條做答案。server經過http訪問，如http://127.0.0.1:8765/?q=hello 。中文需轉urlcode發送，java端讀取按urlcode解析，server啓動方法：apache

java -cp $CLASSPATH:./lib/:./chatbotv1-0.0.1-SNAPSHOT.jar com.shareditor.chatbotv1.Searcher

聊天界面。一個展現聊天內容框框，選擇ckeditor，支持html格式內容展現，一個輸入框和發送按鈕，html代碼：

<div class="col-sm-4 col-xs-10">
    <div class="row">
        <textarea id="chatarea">
            <div style='color: blue; text-align: left; padding: 5px;'>機器人: 喂，大哥您好，您終於肯跟我聊天了,來侃侃唄,我來者不拒!</div>
            <div style='color: blue; text-align: left; padding: 5px;'>機器人: 啥?你問我怎麼這麼聰明會聊天?由於我剛剛吃了一堆影視劇字幕!</div>
        </textarea>
    </div>
    <br />

    <div class="row">
        <div class="input-group">
            <input type="text" id="input" class="form-control" autofocus="autofocus" onkeydown="submitByEnter()" />
            <span class="input-group-btn">
            <button class="btn btn-default" type="button" onclick="submit()">發送</button>
          </span>
        </div>
    </div>
</div>

<script type="text/javascript">

        CKEDITOR.replace('chatarea',
                {
                    readOnly: true,
                    toolbar: ['Source'],
                    height: 500,
                    removePlugins: 'elementspath',
                    resize_enabled: false,
                    allowedContent: true
                });
   
</script>

調用聊天server，要一個發送請求獲取結果控制器：

public function queryAction(Request $request)
{
    $q = $request->get('input');
    $opts = array(
        'http'=>array(
            'method'=>"GET",
            'timeout'=>60,
        )
    );
    $context = stream_context_create($opts);
    $clientIp = $request->getClientIp();
    $response = file_get_contents('http://127.0.0.1:8765/?q=' . urlencode($q) . '&clientIp=' . $clientIp, false, $context);
    $res = json_decode($response, true);
    $total = $res['total'];
    $result = '';
    if ($total > 0) {
        $result = $res['result'][0]['answer'];
    }
    return new Response($result);
}

控制器路由配置：

chatbot_query:
    path:     /chatbot/query
    defaults: { _controller: AppBundle:ChatBot:query }

聊天server響應時間比較長，不致使web界面卡住，執行submit時異步發請求和收結果：

var xmlHttp;
function submit() {
    if (window.ActiveXObject) {
        xmlHttp = new ActiveXObject("Microsoft.XMLHTTP");
    }
    else if (window.XMLHttpRequest) {
        xmlHttp = new XMLHttpRequest();
    }
    var input = $("#input").val().trim();
    if (input == '') {
        jQuery('#input').val('');
        return;
    }
    addText(input, false);
    jQuery('#input').val('');
    var datastr = "input=" + input;
    datastr = encodeURI(datastr);
    var url = "/chatbot/query";
    xmlHttp.open("POST", url, true);
    xmlHttp.onreadystatechange = callback;
    xmlHttp.setRequestHeader("Content-type", "application/x-www-form-urlencoded");
    xmlHttp.send(datastr);
}

function callback() {
    if (xmlHttp.readyState == 4 && xmlHttp.status == 200) {
        var responseText = xmlHttp.responseText;
        addText(responseText, true);
    }
}

addText往ckeditor添加一段文本：

function addText(text, is_response) {
    var oldText = CKEDITOR.instances.chatarea.getData();
    var prefix = '';
    if (is_response) {
        prefix = "<div style='color: blue; text-align: left; padding: 5px;'>機器人: "
    } else {
        prefix = "<div style='color: darkgreen; text-align: right; padding: 5px;'>我: "
    }
    CKEDITOR.instances.chatarea.setData(oldText + "" + prefix + text + "</div>");
}

代碼： https://github.com/warmheartli/ChatBotCourse https://github.com/warmheartli/shareditor.com

效果演示：http://www.shareditor.com/chatbot/

導流。統計網站流量狀況。cnzz統計看最近半個月受訪頁面流量狀況，用戶訪問集中頁面。增長圖庫動態按鈕。吸引用戶點擊，在每一個頁面右下角放置動態小圖標，頁面滾動它不動，用戶點了直接跳到想要引流的頁面。搜客服漂浮代碼。建立js文件，lrtk.js ：

$(function()
{
    var tophtml="<a href=\"http://www.shareditor.com/chatbot/\" target=\"_blank\"><div id=\"izl_rmenu\" class=\"izl-rmenu\"><div class=\"btn btn-phone\"></div><div class=\"btn btn-top\"></div></div></a>";
    $("#top").html(tophtml);
    $("#izl_rmenu").each(function()
    {
        $(this).find(".btn-phone").mouseenter(function()
        {
            $(this).find(".phone").fadeIn("fast");
        });
        $(this).find(".btn-phone").mouseleave(function()
        {
            $(this).find(".phone").fadeOut("fast");
        });
        $(this).find(".btn-top").click(function()
        {
            $("html, body").animate({
                "scroll-top":0
            },"fast");
        });
    });
    var lastRmenuStatus=false;

    $(window).scroll(function()
    {
        var _top=$(window).scrollTop();
        if(_top>=0)
        {
            $("#izl_rmenu").data("expanded",true);
        }
        else
        {
            $("#izl_rmenu").data("expanded",false);
        }
        if($("#izl_rmenu").data("expanded")!=lastRmenuStatus)
        {
            lastRmenuStatus=$("#izl_rmenu").data("expanded");
            if(lastRmenuStatus)
            {
                $("#izl_rmenu .btn-top").slideDown();
            }
            else
            {
                $("#izl_rmenu .btn-top").slideUp();
            }
        }
    });
});

上半部分定義id=top的div標籤內容。一個id爲izl_rmenu的div，css格式定義在另外一個文件lrtk.css裏：

.izl-rmenu{position:fixed;left:85%;bottom:10px;padding-bottom:73px;z-index:999;}
.izl-rmenu .btn{width:72px;height:73px;margin-bottom:1px;cursor:pointer;position:relative;}
.izl-rmenu .btn-top{background:url(http://www.shareditor.com/uploads/media/default/0001/01/thumb_416_default_big.png) 0px 0px no-repeat;background-size: 70px 70px;display:none;}

下半部分當頁面滾動時div展開。

在全部頁面公共代碼部分增長

<div id="top"></div>

龐大語料庫運用，LSTM-RNN訓練，中文語料轉成算法識別向量形式，最強大word embedding工具word2vec。

word2vec輸入切詞文本文件，影視劇字幕語料庫回車換行分隔完整句子，因此咱們先對其作切詞，word_segment.py文件：

# coding:utf-8

import sys
import importlib
importlib.reload(sys)

import jieba
from jieba import analyse

def segment(input, output):
    input_file = open(input, "r")
    output_file = open(output, "w")
    while True:
        line = input_file.readline()
        if line:
            line = line.strip()
            seg_list = jieba.cut(line)
            segments = ""
            for str in seg_list:
                segments = segments + " " + str
            segments = segments + "\n"
            output_file.write(segments)
        else:
            break
    input_file.close()
    output_file.close()

if __name__ == '__main__':
    if 3 != len(sys.argv):
        print("Usage: ", sys.argv[0], "input output")
        sys.exit(-1)
    segment(sys.argv[1], sys.argv[2]);

使用：

python word_segment.py subtitle/raw_subtitles/subtitle.corpus segment_result

word2vec生成詞向量。word2vec可從https://github.com/warmheartli/ChatBotCourse/tree/master/word2vec獲取，make編譯生成二進制文件。執行：

./word2vec -train ../segment_result -output vectors.bin -cbow 1 -size 200 -window 8 -negative 25 -hs 0 -sample 1e-4 -threads 20 -binary 1 -iter 15

生成vectors.bin詞向量，二進制格式，word2vec自帶distance工具來驗證：

./distance vectors.bin

詞向量二進制文件格式加載。word2vec生成詞向量二進制格式：詞數目(空格)向量維度。加載詞向量二進制文件python腳本：

# coding:utf-8

import sys
import struct
import math
import numpy as np

reload(sys)
sys.setdefaultencoding( "utf-8" )

max_w = 50
float_size = 4

def load_vectors(input):
    print "begin load vectors"

    input_file = open(input, "rb")

    # 獲取詞表數目及向量維度
    words_and_size = input_file.readline()
    words_and_size = words_and_size.strip()
    words = long(words_and_size.split(' ')[0])
    size = long(words_and_size.split(' ')[1])
    print "words =", words
    print "size =", size

    word_vector = {}

    for b in range(0, words):
        a = 0
        word = ''
        # 讀取一個詞
        while True:
            c = input_file.read(1)
            word = word + c
            if False == c or c == ' ':
                break
            if a < max_w and c != '\n':
                a = a + 1
        word = word.strip()

        # 讀取詞向量
        vector = np.empty([200])
        for index in range(0, size):
            m = input_file.read(float_size)
            (weight,) = struct.unpack('f', m)
            vector[index] = weight

        # 將詞及其對應的向量存到dict中
        word_vector[word.decode('utf-8')] = vector

    input_file.close()

    print "load vectors finish"
    return word_vector

if __name__ == '__main__':
    if 2 != len(sys.argv):
        print "Usage: ", sys.argv[0], "vectors.bin"
        sys.exit(-1)
    d = load_vectors(sys.argv[1])
    print d[u'真的']

運行方式以下：

python word_vectors_loader.py vectors.bin

參考資料：

《Python 天然語言處理》

http://www.shareditor.com/blogshow?blogId=113

http://www.shareditor.com/blogshow?blogId=114

http://www.shareditor.com/blogshow?blogId=115

歡迎推薦上海機器學習工做機會，個人微信：qingxingfengzi

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