def __func_tokenizer(text:str,
tokenizer_obj:MecabWrapper,
pos_condition:List[Tuple[str,...]]=None,
is_surface:bool=False)->List[str]:
"""* What you can do
- This is base function tokenizer.
- You use this function with functools.partial
"""
if pos_condition is None:
return tokenizer_obj.tokenize(sentence=text, is_surface=is_surface).convert_list_object()
else:
return tokenizer_obj.tokenize(sentence=text, is_surface=is_surface).filter(pos_condition).convert_list_object()
def basic_example():
# ========================================================
# TOKENIZE
# ========================================================
# input is `unicode` type(in python2x)
# In python3x, you don't mind it
sentence = u'テヘラン（ペルシア語: تهران ; Tehrān Tehran.ogg 発音[ヘルプ/ファイル]/teɦˈrɔːn/、英語:Tehran）は、西アジア、イランの首都でありかつテヘラン州の州都。人口12,223,598人。都市圏人口は13,413,348人に達する。'
# make MecabWrapper object
# osType is generic or centos. it's because Mecab has different system command in CentOs.
# If you're using this in CentsOs, put "centos"
osType = "generic"
# you can choose from "neologd", "all", "ipaddic", "user", ""
# "ipadic" and "" is equivalent
dictType = ""
mecab_wrapper = MecabWrapper(dictType=dictType, osType=osType)
# tokenize sentence. Returned object is list of tuples
tokenized_obj = mecab_wrapper.tokenize(sentence=sentence)
assert isinstance(tokenized_obj, list)
# Returned object is "TokenizedSenetence" class if you put return_list=False
tokenized_obj = mecab_wrapper.tokenize(sentence=sentence, return_list=False)
assert isinstance(tokenized_obj, TokenizedSenetence)
# ========================================================
# FILTERING
# ========================================================
# you can filter tokens by stopwords or POS conditions
# stopword is list objetc
stopwords = [u'テヘラン']
assert isinstance(tokenized_obj, TokenizedSenetence)
# returned object is "FilteredObject" class
filtered_obj = mecab_wrapper.filter(
parsed_sentence=tokenized_obj,
stopwords=stopwords
)
assert isinstance(filtered_obj, FilteredObject)
# pos condition is list of tuples
# You can set POS condition "ChaSen 品詞体系 (IPA品詞体系)" of this page http://www.unixuser.org/~euske/doc/postag/#chasen
pos_condition = [(u'名詞', u'固有名詞'), (u'動詞', u'自立')]
filtered_obj = mecab_wrapper.filter(
parsed_sentence=tokenized_obj,
pos_condition=pos_condition
)
assert isinstance(filtered_obj, FilteredObject)
def basic_example_3x():
# ========================================================
# TOKENIZE
# ========================================================
# In python3x, you don't mind it
sentence = 'テヘラン（ペルシア語: تهران ; Tehrān Tehran.ogg 発音[ヘルプ/ファイル]/teɦˈrɔːn/、英語:Tehran）は、西アジア、イランの首都でありかつテヘラン州の州都。人口12,223,598人。都市圏人口は13,413,348人に達する。'
# make MecabWrapper object
# path where `mecab-config` command exists. You can check it with `which mecab-config`
# default value is '/usr/local/bin'
path_mecab_config='/usr/local/bin'
# you can choose from "neologd", "all", "ipaddic", "user", ""
# "ipadic" and "" is equivalent
dictType = ""
mecab_wrapper = MecabWrapper(dictType=dictType)
# tokenize sentence. Returned object is list of tuples
tokenized_obj = mecab_wrapper.tokenize(sentence=sentence)
assert isinstance(tokenized_obj, list)
# Returned object is "TokenizedSenetence" class if you put return_list=False
tokenized_obj = mecab_wrapper.tokenize(sentence=sentence, return_list=False)
assert isinstance(tokenized_obj, TokenizedSenetence)
# ========================================================
# FILTERING
# ========================================================
# you can filter tokens by stopwords or POS conditions
# stopword is list objetc
stopwords = ['テヘラン']
assert isinstance(tokenized_obj, TokenizedSenetence)
# returned object is "FilteredObject" class
filtered_obj = mecab_wrapper.filter(
parsed_sentence=tokenized_obj,
stopwords=stopwords
)
assert isinstance(filtered_obj, FilteredObject)
# pos condition is list of tuples
# You can set POS condition "ChaSen 品詞体系 (IPA品詞体系)" of this page http://www.unixuser.org/~euske/doc/postag/#chasen
pos_condition = [('名詞', '固有名詞'), ('動詞', '自立')]
filtered_obj = mecab_wrapper.filter(
parsed_sentence=tokenized_obj,
pos_condition=pos_condition
)
assert isinstance(filtered_obj, FilteredObject)
def advanced_example_3x():
# ========================================================
# USE YOUE OWN DICTIONARY
# with your own dictionary, you can force Mecab to make some word into one token
# ========================================================
# make your own "user dictionary" with CSV file
# To know more about this file, see this page(sorry, Japanese only) https://mecab.googlecode.com/svn/trunk/mecab/doc/dic.html
example_user_dict = "userdict.csv"
# set dictType='user' or dictType='all'
# set pathUserDictCsv
mecab_wrapper = MecabWrapper(dictType='user',
pathUserDictCsv=example_user_dict)
sentence = 'テヘランは、西アジア、イランの首都でありかつテヘラン州の州都。人口12,223,598人。都市圏人口は13,413,348人に達する。'
tokenized_obj = mecab_wrapper.tokenize(sentence, return_list=False)
assert isinstance(tokenized_obj, TokenizedSenetence)
for token_obj in tokenized_obj.tokenized_objects:
assert isinstance(token_obj, TokenizedResult)
if token_obj.word_stem == 'ペルシア語':
print(token_obj.word_stem)
def basic_example_3x():
# ========================================================
# TOKENIZE
# ========================================================
# In python3x, you don't mind it
sentence = 'テヘラン（ペルシア語: تهران ; Tehrān Tehran.ogg 発音[ヘルプ/ファイル]/teɦˈrɔːn/、英語:Tehran）は、西アジア、イランの首都でありかつテヘラン州の州都。人口12,223,598人。都市圏人口は13,413,348人に達する。'
# make MecabWrapper object
# path where `mecab-config` command exists. You can check it with `which mecab-config`
# default value is '/usr/local/bin'
path_mecab_config = '/usr/local/bin'
# you can choose from "neologd", "all", "ipaddic", "user", ""
# "ipadic" and "" is equivalent
dictType = ""
mecab_wrapper = MecabWrapper(dictType=dictType)
# tokenize sentence. Returned object is list of tuples
tokenized_obj = mecab_wrapper.tokenize(sentence=sentence, return_list=True)
assert isinstance(tokenized_obj, list)
# Returned object is "TokenizedSenetence" class if you put return_list=False
tokenized_obj = mecab_wrapper.tokenize(sentence=sentence,
return_list=False)
assert isinstance(tokenized_obj, TokenizedSenetence)
# ========================================================
# FILTERING
# ========================================================
# you can filter tokens by stopwords or POS conditions
# stopword is list objetc
stopwords = ['テヘラン']
assert isinstance(tokenized_obj, TokenizedSenetence)
# returned object is "FilteredObject" class
filtered_obj = mecab_wrapper.filter(parsed_sentence=tokenized_obj,
stopwords=stopwords)
assert isinstance(filtered_obj, FilteredObject)
# pos condition is list of tuples
# You can set POS condition "ChaSen 品詞体系 (IPA品詞体系)" of this page http://www.unixuser.org/~euske/doc/postag/#chasen
pos_condition = [('名詞', '固有名詞'), ('動詞', '自立')]
filtered_obj = mecab_wrapper.filter(parsed_sentence=tokenized_obj,
pos_condition=pos_condition)
assert isinstance(filtered_obj, FilteredObject)
### You can write chain expression on init-instance -> tokenize -> filtering -> list ###
filtered_result = MecabWrapper(
dictType=dictType,
path_mecab_config=path_mecab_config).tokenize(sentence).filter(
pos_condition=pos_condition).convert_list_object()
assert isinstance(filtered_result, list)
print(filtered_result)
def advanced_example_3x():
# ========================================================
# USE YOUE OWN DICTIONARY
# with your own dictionary, you can force Mecab to make some word into one token
# ========================================================
# make your own "user dictionary" with CSV file
# To know more about this file, see this page(sorry, Japanese only) https://mecab.googlecode.com/svn/trunk/mecab/doc/dic.html
example_user_dict = "userdict.csv"
# set dictType='user' or dictType='all'
# set pathUserDictCsv
mecab_wrapper = MecabWrapper(
dictType='user',
pathUserDictCsv=example_user_dict
)
sentence = 'テヘラン（ペルシア語: تهران ; Tehrān Tehran.ogg 発音[ヘルプ/ファイル]/teɦˈrɔːn/、英語:Tehran）は、西アジア、イランの首都でありかつテヘラン州の州都。人口12,223,598人。都市圏人口は13,413,348人に達する。'
tokenized_obj = mecab_wrapper.tokenize(sentence, return_list=False)
assert isinstance(tokenized_obj, TokenizedSenetence)
for token_obj in tokenized_obj.tokenized_objects:
assert isinstance(token_obj, TokenizedResult)
if token_obj.word_stem == 'ペルシア語':
print(token_obj.word_stem)
class MecabTokenizer:
def __init__(self, dict_type=None):
if dict_type:
self.tokenizer = MecabWrapper(dictType=dict_type)
else:
self.tokenizer = MecabWrapper(dictType=None)
def tokenize(self, text):
tokenized_objects = self.tokenizer.tokenize(text).tokenized_objects
return [
dict(analyzed_line=obj.analyzed_line,
word_surface=obj.word_surface,
word_stem=obj.word_stem,
pos=list(obj.tuple_pos),
misc_info=obj.misc_info) for obj in tokenized_objects
]
def __init__(self, dict_type=None):
if dict_type:
self.tokenizer = MecabWrapper(dictType=dict_type)
else:
self.tokenizer = MecabWrapper(dictType=None)
def basic_example_mecab_2x():
# ========================================================
# TOKENIZE
# ========================================================
# input is `unicode` type(in python2x)
sentence = u'テヘランは、西アジア、イランの首都でありかつテヘラン州の州都。人口12,223,598人。都市圏人口は13,413,348人に達する。'
# make MecabWrapper object
# you can choose from "neologd", "all", "ipadic", "user", "", None
# "ipadic" and "" is equivalent
dictType = "neologd"
mecab_wrapper = MecabWrapper(dictType=dictType)
# tokenize sentence. Returned object is list of tuples
tokenized_obj = mecab_wrapper.tokenize(sentence=sentence, return_list=True)
assert isinstance(tokenized_obj, list)
# Returned object is "TokenizedSenetence" class if you put return_list=False
tokenized_obj = mecab_wrapper.tokenize(sentence=sentence,
return_list=False)
assert isinstance(tokenized_obj, TokenizedSenetence)
# ========================================================
# FILTERING
# ========================================================
# you can filter tokens by stopwords or POS conditions
# stopword is list objetc
stopwords = [u'テヘラン']
assert isinstance(tokenized_obj, TokenizedSenetence)
# returned object is "FilteredObject" class
filtered_obj = mecab_wrapper.filter(parsed_sentence=tokenized_obj,
stopwords=stopwords)
assert isinstance(filtered_obj, FilteredObject)
#
print('-' * 30)
print(u'Mecab Demo')
for token_obj in filtered_obj.tokenized_objects:
assert isinstance(token_obj, TokenizedResult)
print(u'word_stem:{}, word_surafce:{}, pos:{}'.format(
token_obj.word_stem, token_obj.word_surface, token_obj.tuple_pos))
# pos condition is list of tuples
# You can set POS condition "ChaSen 品詞体系 (IPA品詞体系)" of this page http://www.unixuser.org/~euske/doc/postag/#chasen
pos_condition = [(u'名詞', u'固有名詞'), (u'動詞', u'自立')]
filtered_obj = mecab_wrapper.filter(parsed_sentence=tokenized_obj,
pos_condition=pos_condition)
assert isinstance(filtered_obj, FilteredObject)
print('-' * 30)
print(u'Mecab Filtering Demo')
for token_obj in filtered_obj.tokenized_objects:
assert isinstance(token_obj, TokenizedResult)
print(u'word_stem:{}, word_surafce:{}, pos:{}'.format(
token_obj.word_stem, token_obj.word_surface, token_obj.tuple_pos))
### You can write chain expression on init-instance -> tokenize -> filtering -> list ###
filtered_result = MecabWrapper(
dictType=dictType).tokenize(sentence).filter(
pos_condition=pos_condition).convert_list_object()
assert isinstance(filtered_result, list)
print(filtered_result)
"""In this example, you see how to get wikipedia-liked information from Japanese sentence
"""
# ------------------------------------------------------------
# PARAMETERS
path_model_file = '../bin/entity_vector/entity_vector.model.bin'
dict_type = 'neologd'
path_mecab_config = '/usr/local/bin/'
pos_condition = [('名詞', )]
mysql_username = '******'
mysql_hostname = 'localhost'
mysql_password = '******'
mysql_db_name = 'wikipedia'
# ------------------------------------------------------------
entity_linking_model = load_entity_model(path_model_file)
mecab_tokenizer = MecabWrapper(dict_type, path_mecab_config=path_mecab_config)
model_object = load_entity_model(path_entity_model=path_model_file, is_use_cache=True) # type: Word2Vec
mysql_connector = initialize_pymysql_connector(hostname=mysql_hostname,
user_name=mysql_username,
password=mysql_password,
dbname=mysql_db_name)
input_sentence = "かつてはイルモア、WCMといったプライベーターがオリジナルマシンで参戦していたほか、カワサキがワークス・チームを送り込んでいたが、2016年現在出場しているのはヤマハ、ホンダ、スズキ、ドゥカティ、アプリリアの5メーカーと、ワークスマシンの貸与等を受けられるサテライトチームとなっている。"
filtered_nouns = mecab_tokenizer.filter(
parsed_sentence=mecab_tokenizer.tokenize(sentence=input_sentence,return_list=False),
pos_condition=pos_condition).convert_list_object()
sequence_score_ojects = predict_japanese_wiki_names_with_wikidump(input_tokens=filtered_nouns,
wikipedia_db_connector=mysql_connector,
entity_vector_model=entity_linking_model,
is_use_cache=True,
PATH_TRAINING_TEXT = './wikipedia_data/wikipedia-full.json'
PATH_TEST_TEXT = './wikipedia_data/wikipedia-evaluation-full.json'
PATH_ENTITY_VECTOR = './entity_vector/entity_vector.model.bin'
PATH_SAVE_TARINED_MODEL = './trained_auto_encoder.h5'
POS_CONDITION = [('名詞',), ('動詞', '自立'), ('形容詞', '自立'), ('副詞',), ('助動詞',), ('連体詞',)]
## check file existing ##
if not os.path.exists(PATH_TRAINING_TEXT):
raise FileExistsError()
if not os.path.exists(PATH_ENTITY_VECTOR):
raise FileExistsError()
## initialize tokenizer funtion ##
tokenizer_obj = MecabWrapper(dictType='neologd')
get_token = partial(__func_tokenizer,
tokenizer_obj=tokenizer_obj,
pos_condition=POS_CONDITION,
is_surface=False)
## load word embedding ##
try:
embedding_model = KeyedVectors.load_word2vec_format(PATH_ENTITY_VECTOR,
**{'binary': True, 'unicode_errors': 'ignore'})
except:
embedding_model = Word2Vec.load_word2vec_format(PATH_ENTITY_VECTOR,
**{'binary': True, 'unicode_errors': 'ignore'})
## make training data ##
def basic_example_mecab_2x():
# ========================================================
# TOKENIZE
# ========================================================
# input is `unicode` type(in python2x)
sentence = u'テヘラン（ペルシア語: تهران ; Tehrān Tehran.ogg 発音[ヘルプ/ファイル]/teɦˈrɔːn/、英語:Tehran）は、西アジア、イランの首都でありかつテヘラン州の州都。人口12,223,598人。都市圏人口は13,413,348人に達する。'
# make MecabWrapper object
# path where `mecab-config` command exists. You can check it with `which mecab-config`
# default value is '/usr/local/bin'
path_mecab_config='/usr/local/bin'
# you can choose from "neologd", "all", "ipaddic", "user", ""
# "ipadic" and "" is equivalent
dictType = "neologd"
mecab_wrapper = MecabWrapper(dictType=dictType, path_mecab_config=path_mecab_config)
# tokenize sentence. Returned object is list of tuples
tokenized_obj = mecab_wrapper.tokenize(sentence=sentence)
assert isinstance(tokenized_obj, list)
# Returned object is "TokenizedSenetence" class if you put return_list=False
tokenized_obj = mecab_wrapper.tokenize(sentence=sentence, return_list=False)
assert isinstance(tokenized_obj, TokenizedSenetence)
# ========================================================
# FILTERING
# ========================================================
# you can filter tokens by stopwords or POS conditions
# stopword is list objetc
stopwords = [u'テヘラン']
assert isinstance(tokenized_obj, TokenizedSenetence)
# returned object is "FilteredObject" class
filtered_obj = mecab_wrapper.filter(
parsed_sentence=tokenized_obj,
stopwords=stopwords
)
assert isinstance(filtered_obj, FilteredObject)
#
print('-'*30)
print(u'Mecab Demo')
for token_obj in filtered_obj.tokenized_objects:
assert isinstance(token_obj, TokenizedResult)
print(u'word_stem:{}, word_surafce:{}, pos:{}'.format(
token_obj.word_stem,
token_obj.word_surface,
token_obj.tuple_pos))
# pos condition is list of tuples
# You can set POS condition "ChaSen 品詞体系 (IPA品詞体系)" of this page http://www.unixuser.org/~euske/doc/postag/#chasen
pos_condition = [(u'名詞', u'固有名詞'), (u'動詞', u'自立')]
filtered_obj = mecab_wrapper.filter(
parsed_sentence=tokenized_obj,
pos_condition=pos_condition
)
assert isinstance(filtered_obj, FilteredObject)
print('-'*30)
print(u'Mecab Filtering Demo')
for token_obj in filtered_obj.tokenized_objects:
assert isinstance(token_obj, TokenizedResult)
print(u'word_stem:{}, word_surafce:{}, pos:{}'.format(
token_obj.word_stem,
token_obj.word_surface,
token_obj.tuple_pos))