def Begin_to_identify(request): # index页面需要一开始就加载的内容写在这里
context = {}
ctx = {}
if request.POST:
key = request.POST["user_text"]
thu1 = pre_load_thu
# 使用thulac进行分词 TagList[i][0]代表第i个词
# TagList[i][1]代表第i个词的词性
key = key.strip()
TagList = thu1.cut(key, text=False)#[[词，词性], [词，词性], []]
text = ""
NE_List = get_NE(key) # 获取实体列表
for pair in NE_List: # 根据实体列表，显示各个实体
if pair[1] == 0:
text += pair[0]
continue
if temporaryok(pair[1]): # 判断实体词性
# text += "<a href='#' data-original-title='" + get_explain(
# pair[1]
# ) + "(暂无资料)' data-placement='top' data-trigger='hover' data-content='" + get_detail_explain(pair[1]) + "' class='popovers'>" + pair[0] + "</a>"
# continue
text += "<a href='#' data-original-title='" + pair[1]+ "(暂无资料)' data-placement='top' data-trigger='hover' data-content='" + pair[1] + "' class='popovers'>" + pair[0] + "</a>"
continue
# text += "<a href='detail.html?title=" + pair[0] + "' data-original-title='" + get_explain(
# pair[1]) + "' data-placement='top' data-trigger='hover' data-content='" + get_detail_explain(pair[1]) + "' class='popovers'>" + pair[0] + "</a>"
# "http://stockdata.stock.hexun.com/gszl/s000001.shtml"
text += "<a href='http://stockdata.stock.hexun.com/gszl/s"+str(pair[1])+".shtml'>"+str(pair[0])+"</a>"
# text += "<a href='http://stockdata.stock.hexun.com/gszl/s"+str(pair[1])+".shtml'>"+str(pair[0])+"</a>"
# text += "<a href='detail.html?title=" + pair[0] + "' data-original-title='" +pair[1]+"' data-placement='top' data-trigger='hover' data-content='" + pair[1]+ "' class='popovers'>" + pair[0] + "</a>"
# <a href="detail.html?title=平安银行 data-original-title=类别 data-placement="top" data-trigger="hover" data-content="类别描述" class="popovers" ">平安银行<a>
# 跳转链接， 应该只是跳转个链接带个titile, 这些属性应该是在<a>标签之上的。
ctx['rlt'] = text # 将实体对应类别和描述，+ 对应单词放入ctx字典，以key=rlt进行查询
seg_word = ""
length = len(TagList) # TagList分词后的数量
for t in TagList: # 测试打印词性序列
seg_word += t[0] + " <strong><small>[" + t[1] + "]</small></strong> " # 将单词和词向进行添加标签
seg_word += "" # 后面加入""
ctx['seg_word'] = seg_word # 以seg_word的key进行查询
return render(request, "index.html", ctx)#返回主页面
def ER_post2(request):
"""
sdf
:param request:
:return:
"""
global parse_util
ctx = {}
if request.POST:
org_text = request.POST['user_text']
text = ""
if parse_util is None:
parse_util = ParseUtil.Parse_Util()
words, postags, netags, arcs = parse_util.parse_sentence(org_text)
NE_List = get_NE_List(words, netags)
for pair in NE_List: # 根据实体列表，显示各个实体
if pair[1] == 'O':
text += pair[0]
continue
if temporaryok(pair[1]):
text += "<a href='#' data-original-title='" + get_explain(
pair[1]
) + "(暂无资料)' data-placement='top' data-trigger='hover' data-content='" + get_detail_explain(
pair[1]) + "' class='popovers'>" + pair[0] + "</a>"
continue
text += "<a href='detail.html?title=" + pair[
0] + "' data-original-title='" + get_explain(
pair[1]
) + "' data-placement='top' data-trigger='hover' data-content='" + get_detail_explain(
pair[1]) + "' class='popovers'>" + pair[0] + "</a>"
ctx['rlt'] = text
seg_word = ""
for index in range(len(words)): # 测试打印词性序列
seg_word += words[index] + " <strong><small>[" + postags[
index] + "]</small></strong> "
seg_word += ""
ctx['seg_word'] = seg_word
return render(request, "index.html", ctx)
def ER_post(request):
ctx ={}
if request.POST:
key = request.POST['user_text']
thu1 = pre_load_thu #提前加载好了
# 使用thulac进行分词 TagList[i][0]代表第i个词
# TagList[i][1]代表第i个词的词性
key = key.strip()
TagList = thu1.cut(key, text=False)
text = ""
NE_List = get_NE(key) #获取实体列表
for pair in NE_List: #根据实体列表，显示各个实体
if pair[1] == 0:
text += pair[0]
continue
if temporaryok(pair[1]):
text += "<a href='#' data-original-title='" + get_explain(pair[1]) + "(暂无资料)' data-placement='top' data-trigger='hover' data-content='"+get_detail_explain(pair[1])+"' class='popovers'>" + pair[0] + "</a>"
continue
text += "<a href='detail.html?title=" + pair[0] + "' data-original-title='" + get_explain(pair[1]) + "' data-placement='top' data-trigger='hover' data-content='"+get_detail_explain(pair[1])+"' class='popovers'>" + pair[0] + "</a>"
ctx['rlt'] = text
seg_word = ""
length = len(TagList)
for t in TagList: #测试打印词性序列
seg_word += t[0]+" <strong><small>["+t[1]+"]</small></strong> "
seg_word += ""
ctx['seg_word'] = seg_word
return render(request, "index.html", ctx)
def ER_post(request):
ctx = {}
if request.POST:
key = request.POST['user_text']
thu1 = pre_load_thu #提前加载好了
# 使用thulac进行分词 TagList[i][0]代表第i个词
# TagList[i][1]代表第i个词的词性
key = key.encode('utf-8').strip()
TagList = thu1.cut(key, text=False)
text = ""
NE_List = get_NE(key) #获取实体列表
for pair in NE_List: #根据实体列表，显示各个实体
if pair[1] == 0:
text += pair[0]
continue
if temporaryok(pair[1]):
text += "<a href='#' data-original-title='" + get_explain(
pair[1]
) + "(暂无资料)' data-placement='top' data-trigger='hover' data-content='" + get_detail_explain(
pair[1]) + "' class='popovers'>" + pair[0] + "</a>"
continue
text += "<a href='detail.html?title=" + pair[
0] + "' data-original-title='" + get_explain(
pair[1]
) + "' data-placement='top' data-trigger='hover' data-content='" + get_detail_explain(
pair[1]) + "' class='popovers'>" + pair[0] + "</a>"
ctx['rlt'] = text
# while i < length:
# # 尝试将2个词组合，若不是NE则组合一个，还不是就直接打印文本
# p1 = TagList[i][0]
# p2 = "*-" # 保证p2没被赋值时，p1+p2必不存在
# if i+1 < length:
# p2 = TagList[i+1][0]
#
# t1 = TagList[i][1]
# t2 = "*-"
# if i+1 < length:
# t2 = TagList[i+1][1]
#
# p = p1 + p2
# if i+1 < length and preok(t1) and nowok(t2):
# answer = db.matchHudongItembyTitle(p)
# if answer != None:
# text += "<a href='detail.html?title=" + str(p) + "' data-toggle='tooltip' title='" + get_explain(t2) + "'>" + p + "</a>"
# i += 2
# continue
#
# p = p1
# if nowok(t1):
# answer = db.matchHudongItembyTitle(p)
# if answer != None:
# text += "<a href='detail.html?title=" + str(p) + "' data-toggle='tooltip' title='" + get_explain(t1) + "'>" + p + "</a>"
# i += 1
# continue
# elif temporaryok(t1):
# text += "<a href='#' data-toggle='tooltip' title='" + get_explain(t1) + "(暂无资料)'>" + p + "</a>"
# i += 1
# continue
#
#
# i += 1
# text += str(p)
seg_word = ""
length = len(TagList)
for t in TagList: #测试打印词性序列
seg_word += t[0] + " <strong><small>[" + t[
1] + "]</small></strong> "
seg_word += ""
ctx['seg_word'] = seg_word
return render(request, "index.html", ctx)
def ER_post(request):
ctx ={}
if request.POST:
key = request.POST['user_text']
thu1 = pre_load_thu #提前加载好了
# 使用thulac进行分词 TagList[i][0]代表第i个词
# TagList[i][1]代表第i个词的词性
key = key.strip()
TagList = thu1.cut(key, text=False)
text = ""
NE_List = get_NE(key) #获取实体列表
for pair in NE_List: #根据实体列表，显示各个实体
if pair[1] == 0:
text += pair[0]
continue
if temporaryok(pair[1]):
text += "<a href='#' data-original-title='" + get_explain(pair[1]) + "(暂无资料)' data-placement='top' data-trigger='hover' data-content='"+get_detail_explain(pair[1])+"' class='popovers'>" + pair[0] + "</a>"
continue
text += "<a href='detail.html?title=" + pair[0] + "' data-original-title='" + get_explain(pair[1]) + "' data-placement='top' data-trigger='hover' data-content='"+get_detail_explain(pair[1])+"' class='popovers'>" + pair[0] + "</a>"
ctx['rlt'] = text
# while i < length:
# # 尝试将2个词组合，若不是NE则组合一个，还不是就直接打印文本
# p1 = TagList[i][0]
# p2 = "*-" # 保证p2没被赋值时，p1+p2必不存在
# if i+1 < length:
# p2 = TagList[i+1][0]
#
# t1 = TagList[i][1]
# t2 = "*-"
# if i+1 < length:
# t2 = TagList[i+1][1]
#
# p = p1 + p2
# if i+1 < length and preok(t1) and nowok(t2):
# answer = db.matchHudongItembyTitle(p)
# if answer != None:
# text += "<a href='detail.html?title=" + str(p) + "' data-toggle='tooltip' title='" + get_explain(t2) + "'>" + p + "</a>"
# i += 2
# continue
#
# p = p1
# if nowok(t1):
# answer = db.matchHudongItembyTitle(p)
# if answer != None:
# text += "<a href='detail.html?title=" + str(p) + "' data-toggle='tooltip' title='" + get_explain(t1) + "'>" + p + "</a>"
# i += 1
# continue
# elif temporaryok(t1):
# text += "<a href='#' data-toggle='tooltip' title='" + get_explain(t1) + "(暂无资料)'>" + p + "</a>"
# i += 1
# continue
#
#
# i += 1
# text += str(p)
seg_word = ""
length = len(TagList)
for t in TagList: #测试打印词性序列
seg_word += t[0]+" <strong><small>["+t[1]+"]</small></strong> "
seg_word += ""
ctx['seg_word'] = seg_word
return render(request, "index.html", ctx)
#过滤掉<doc > </doc> 等无用行
if (len(line) < 2 or line[0:4] == '<doc'
or line[0:6] == "</doc>"):
continue
#分句
statements = CutStatements(line)
for statement in statements:
#分词
cutResult = get_NE(statement.strip())
#得到每句话的实体列表后，两两匹配查询是否具有某种关系,如果有的话就写到文件中
#entityList 存储实体列表和实体出现的位置,entity1存储实体名称，entity1Index存储实体位置
entityList = []
nowIndex = -1
for word in cutResult:
if (word[1] != 0
and not temporaryok(word[1])):
entity1Index = statement.index(
word[0], nowIndex + 1)
entityList.append({
'entity1':
word[0],
'entity1Index':
entity1Index
})
nowIndex = entity1Index + len(
word[0]) - 1
entityNumber = len(entityList)
for i in range(entityNumber):
answer = None
#answer = entityRelationDict.get(entityList[i].get('entity1'))
if(count%100 == 0):
print(filePath+" "+str(count))
#过滤掉<doc > </doc> 等无用行
if(len(line)< 2 or line[0:4] == '<doc' or line[0:6] == "</doc>"):
continue
#分句
statements = CutStatements(line)
for statement in statements:
#分词
cutResult = get_NE(statement.strip())
#得到每句话的实体列表后，两两匹配查询是否具有某种关系,如果有的话就写到文件中
#entityList 存储实体列表和实体出现的位置,entity1存储实体名称，entity1Index存储实体位置
entityList = []
nowIndex = -1
for word in cutResult:
if(word[1]!=0 and not temporaryok(word[1])):
entity1Index = statement.index(word[0],nowIndex+1)
entityList.append({'entity1':word[0],'entity1Index':entity1Index})
nowIndex = entity1Index+len(word[0])-1
entityNumber = len(entityList)
for i in range(entityNumber):
answer = None
#answer = entityRelationDict.get(entityList[i].get('entity1'))
#if(entityRelationDict.get(entityList[i].get('entity1')) is None):
answer = db.findRelationBetweenEntities(entityList[i].get('entity1'))
#entityRelationDict[entityList[i].get('entity1')] = answer
for relation in answer:
#对neo4j的返回值进行处理，原来的返回值中包含一些没用的字符，最终得到的关系是rel,实体是entity2
if(len(str(relation['rel']).split("\"")) < 2):
continue