学习笔记CB011:lucene搜索引擎库、IKAnalyzer中文切词工具、检索服务、查询索引、导流、word2vec

影视剧字幕聊天语料库特点，把影视剧说话内容一句一句以回车换行罗列三千多万条中国话，相邻第二句很可能是第一句最好回答。一个问句有很多种回答，可以根据相关程度以及历史聊天记录所有回答排序，找到最优，是一个搜索排序过程。

lucene+ik。lucene开源免费搜索引擎库，java语言开发。ik IKAnalyzer，开源中文切词工具。语料库切词建索引，文本搜索做文本相关性检索，把下一句取出作答案候选集，答案排序，问题分析。

建索引。eclipse创建maven工程，maven自动生成pom.xml文件，配置包依赖信息，dependencies标签中添加依赖：

<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标签增加配置，依赖jar包自动拷贝lib目录：

<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>

下载ik源代码把src/org目录拷到chatbotv1工程src/main/java下，刷新maven工程。

com.shareditor.chatbotv1包下maven自动生成App.java，改成Indexer.java：

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();

编译拷贝src/main/resources所有文件到target目录，target目录执行

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

生成索引目录index通过lukeall-4.9.0.jar查看。

检索服务。netty创建http服务server，代码在https://github.com/warmheartli/ChatBotCourse的chatbotv1目录：

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);

查询索引，query词做切词拼lucene query，检索索引question字段，匹配返回answer字段值作候选集，挑出候选集一条作答案。server通过http访问，如http://127.0.0.1:8765/?q=hello 。中文需转urlcode发送，java端读取按urlcode解析，server启动方法：

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

聊天界面。一个展示聊天内容框框，选择ckeditor，支持html格式内容展示，一个输入框和发送按钮，html代码：

<div class="col-sm-4 col-xs-10">
    <div class="row">
        <textarea id="chatarea">
            <div style='color: blue; text-align: left; padding: 5px;'>机器人: 喂，大哥您好，您终于肯跟我聊天了,来侃侃呗,我来者不拒!</div>
            <div style='color: blue; text-align: left; padding: 5px;'>机器人: 啥?你问我怎么这么聪明会聊天?因为我刚刚吃了一堆影视剧字幕!</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()">发送</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>

调用聊天server，要一个发送请求获取结果控制器：

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响应时间比较长，不导致web界面卡住，执行submit时异步发请求和收结果：

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;'>机器人: "
    } else {
        prefix = "<div style='color: darkgreen; text-align: right; padding: 5px;'>我: "
    }
    CKEDITOR.instances.chatarea.setData(oldText + "" + prefix + text + "</div>");
}

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

导流。统计网站流量情况。cnzz统计看最近半个月受访页面流量情况，用户访问集中页面。增加图库动态按钮。吸引用户点击，在每个页面右下角放置动态小图标，页面滚动它不动，用户点了直接跳到想要引流的页面。搜客服漂浮代码。
创建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();
            }
        }
    });
});

上半部分定义id=top的div标签内容。一个id为izl_rmenu的div，css格式定义在另一个文件lrtk.css里：

.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;}

下半部分当页面滚动时div展开。

在所有页面公共代码部分增加

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

庞大语料库运用，LSTM-RNN训练，中文语料转成算法识别向量形式，最强大word embedding工具word2vec。

word2vec输入切词文本文件，影视剧字幕语料库回车换行分隔完整句子，所以我们先对其做切词，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生成词向量。word2vec可从https://github.com/warmheartli/ChatBotCourse/tree/master/word2vec获取，make编译生成二进制文件。
执行：

./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词向量，二进制格式，word2vec自带distance工具来验证：

./distance vectors.bin

词向量二进制文件格式加载。word2vec生成词向量二进制格式：词数目(空格)向量维度。
加载词向量二进制文件python脚本：

# 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")

    # 获取词表数目及向量维度
    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 = ''
        # 读取一个词
        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()

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

        # 将词及其对应的向量存到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']

运行方式如下：

python word_vectors_loader.py vectors.bin

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