class NRConstant(object):
# 本词典专注的词的ID
WORD_ID = CoreDictionary.getWordID(Predefine.TAG_PEOPLE)
# 本词典专注的词的属性
ATTRIBUTE = CoreDictionary.get2(WORD_ID)
class PlaceDictionary(object):
# 地名词典
dictionary = NSDictionary()
# 转移矩阵词典
transformMatrixDictionary = TransformMatrixDictionary()
# AC算法用到的Trie树
trie = AhoCorasickDoubleArrayTrie()
# 本词典专注的词的ID
WORD_ID = CoreDictionary.getWordID(Predefine.TAG_PLACE)
# 本词典专注的词的属性
ATTRIBUTE = CoreDictionary.get2(WORD_ID)
def __init__(self):
self.load()
def load(self):
start = time()
PlaceDictionary.dictionary.load(Config.PlaceDictionaryPath)
Predefine.logger.info("%s加载成功，耗时%fms" % (Config.PlaceDictionaryPath,
(time() - start) * 1000))
print "%s加载成功，耗时%fms" % (Config.PlaceDictionaryPath,
(time() - start) * 1000)
PlaceDictionary.transformMatrixDictionary = PlaceDictionary.transformMatrixDictionary.init1(
NS)
PlaceDictionary.transformMatrixDictionary.load(
Config.PlaceDictionaryTrPath)
init_dict = {}
init_dict["CDEH"] = "CDEH"
init_dict["CDH"] = "CDH"
init_dict["CH"] = "CH"
init_dict["GH"] = "GH"
PlaceDictionary.trie.build(TreeMap(init_dict))
@staticmethod
def parsePattern(nsList, vertexList, wordNetOptimum, wordNetAll, pld_obj):
"""
模式匹配
:param nsList: 确定的标注序列
:param wordNetOptimum: 原始的未加角色标注的序列
:param wordNetAll: 待优化的图
:return:
"""
sbPattern = ""
for ns in nsList:
sbPattern += str(ns)
pattern = str(sbPattern)
wordList = []
for i in range(len(vertexList)):
wordList.append(vertexList[i].realword)
wordArray = np.array(wordList)
PlaceDictionary.trie.parseText1(pattern, wordArray, pld_obj,
wordNetOptimum, wordNetAll)
@staticmethod
def isBadCase(name):
"""
因为任何算法都无法解决100%的问题，总是有一些bad case，这些bad case会以“盖公章 A 1”的形式加入词典中<BR>
这个方法返回是否是bad case
:param name:
:return:
"""
nrEnumItem = None
place_list = PlaceDictionary.dictionary.get(name)
if place_list is not None:
initdict = dict(place_list)
nrEnumItem = EnumItem().init3(initdict)
if nrEnumItem is None:
return False
return nrEnumItem.containsLabel(NS.Z)