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
由 清醒疯子 发布于 2018年04月21日无人欣赏。
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>
https://storage.googleapis.com/google-code-archive-downloads/v2/code.google.com/ik-analyzer/IK%20Analyzer%202012FFhf1source.rar 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
效果演示：http://www.shareditor.com/chatbot/
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
参考资料：
《Python 自然语言处理》
http://www.shareditor.com/blogshow?blogId=113
http://www.shareditor.com/blogshow?blogId=114
http://www.shareditor.com/blogshow?blogId=115
欢迎推荐上海机器学习工作机会，我的微信：qingxingfengzi
暂无回复