class SequenceTagger(TaggerI):
""" wrapper for [Wapiti](http://wapiti.limsi.fr) sequence tagger
>>> tagger = SequenceTagger(patterns=['*', 'u:word-%x[0,0]'])
>>> tagger.train([[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]])
>>> tagger.tag_sents([['من', 'به', 'مدرسه', 'رفته_بودم', '.']])
[[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]]
>>> tagger.save_model('resources/test.model')
>>> SequenceTagger(model='resources/test.model').tag_sents([['من', 'به', 'مدرسه', 'رفته_بودم', '.']])
[[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]]
"""
def __init__(self, patterns=[], **options):
from wapiti import Model
self.model = Model(patterns='\n'.join(patterns), **options)
def train(self, sentences):
self.model.train(['\n'.join([' '.join(word) for word in sentence]) for sentence in sentences])
def tag(self, tokens):
return self.tag_sents([tokens])[0]
def save_model(self, filename):
self.model.save(filename)
def tag_sents(self, sentences):
sentences = list(sentences)
lines = '\n\n'.join(['\n'.join(sentence) for sentence in sentences]).replace(' ', '_')
results = self.model.label_sequence(lines).decode('utf8')
tags = iter(results.strip().split('\n'))
return [[(word, next(tags)) for word in sentence] for sentence in sentences]
class SequenceTagger(TaggerI):
""" wrapper for [Wapiti](http://wapiti.limsi.fr) sequence tagger
>>> tagger = SequenceTagger(patterns=['*', 'u:word-%x[0,0]'])
>>> tagger.train([[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]])
>>> tagger.tag_sents([['من', 'به', 'مدرسه', 'رفته_بودم', '.']])
[[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]]
>>> tagger.save_model('resources/test.model')
>>> SequenceTagger(model='resources/test.model').tag_sents([['من', 'به', 'مدرسه', 'رفته_بودم', '.']])
[[('من', 'PRO'), ('به', 'P'), ('مدرسه', 'N'), ('رفته_بودم', 'V'), ('.', 'PUNC')]]
"""
def __init__(self, patterns=[], **options):
from wapiti import Model
self.model = Model(patterns='\n'.join(patterns), **options)
def train(self, sentences):
self.model.train([
'\n'.join([' '.join(word) for word in sentence])
for sentence in sentences
])
def save_model(self, filename):
self.model.save(filename)
def tag_sents(self, sentences):
sentences = list(sentences)
lines = '\n\n'.join(['\n'.join(sentence)
for sentence in sentences]).replace(' ', '_')
results = self.model.label_sequence(lines).decode('utf8')
tags = iter(results.strip().split('\n'))
return [[(word, next(tags)) for word in sentence]
for sentence in sentences]
class WapitiPOSTagger(TaggerI):
"""docstring for WapitiPOSTagger"""
def __init__(self, *args, **kwargs):
if 'model' not in kwargs:
kwargs['model'] = 'resources/model.txt'
if 'pattern' not in kwargs:
kwargs['pattern'] = 'resources/pattern.txt'
super(WapitiPOSTagger, self).__init__()
option_dict = {}
option_dict['pattern'] = kwargs['pattern']
option_dict['model'] = kwargs['model']
self.model = Model(**option_dict)
def tag_sents(self, sents):
for words in sents:
tags = self.model.label_sequence('\n'.join(words)).split('\n')
yield zip(words, tags)
def tag(self, sent):
tags = self.model.label_sequence('\n'.join(sent)).split('\n')
return zip(sent, tags)
class WapitiPOSTagger(TaggerI):
"""docstring for WapitiPOSTagger"""
def __init__(self, *args, **kwargs):
if 'model' not in kwargs:
kwargs['model'] = 'resources/model.txt'
if 'pattern' not in kwargs:
kwargs['pattern'] = 'resources/pattern.txt'
super(WapitiPOSTagger, self).__init__()
option_dict = {}
option_dict['pattern'] = kwargs['pattern']
option_dict['model'] = kwargs['model']
self.model = Model(**option_dict)
def tag_sents(self, sents):
for words in sents:
tags = self.model.label_sequence('\n'.join(words)).split('\n')
yield zip(words, tags)
def tag(self, sent):
tags = self.model.label_sequence('\n'.join(sent)).split('\n')
return zip(sent, tags)
class POSTagger():
def __init__(self,
stanford_postagger_model=None,
wapiti_postagger_model=None,
jar_tagger_path=None,
jdk_variable_path="C:/Program Files/Java/jdk1.8.0_121/bin/java.exe",
tagging_model="wapiti"):
import platform
if platform.system() == "Windows":
self.tagging_model = "stanford"
else:
self.tagging_model = tagging_model
self.dir_path = os.path.dirname(os.path.realpath(__file__)) + "/"
if stanford_postagger_model is None:
self.stanford_postagger_model = self.dir_path + "resource/postagger/NC_model"
else:
self.stanford_postagger_model = stanford_postagger_model
if jar_tagger_path is None:
self.jar_tagger_path = self.dir_path + 'resource/postagger/stanford-postagger.jar'
else:
self.jar_tagger_path = jar_tagger_path
if wapiti_postagger_model is None:
self.wapiti_postagger_model = self.dir_path + "resource/postagger/UPC_full_model_wapiti"
else:
self.wapiti_postagger_model = wapiti_postagger_model
if self.tagging_model == "stanford":
java_path = jdk_variable_path
os.environ['JAVAHOME'] = java_path
self.tagger = StanfordPOSTagger(model_filename=self.stanford_postagger_model,
path_to_jar=self.jar_tagger_path,
encoding='utf-8',
java_options='-mx5000m')
elif self.tagging_model == "wapiti":
from wapiti import Model
self.tagger = Model(model=self.wapiti_postagger_model)
def is_all_latin(self, word):
pattern = '[a-zA-Z]*'
w = re.sub(pattern, '', word)
if len(w) == 0:
return True
else:
return False
def parse(self, token_list):
tagged_tuples = []
if self.tagging_model == "stanford":
postags = self.tagger.tag(token_list)
for element in postags:
tmp = '_'.join(t for t in element)
tmp = tmp.strip("_")
tmp = tmp.split('/')
tag = tmp[-1]
tmp = tmp[:-1]
tmp = '/'.join(i for i in tmp)
tmp = tmp.strip('/')
if self.is_all_latin(tmp):
tagged_tuples.append((tmp, "FW"))
else:
tagged_tuples.append((tmp, tag))
elif self.tagging_model == "wapiti":
sent_line = "\n".join(x for x in token_list)
postags = self.tagger.label_sequence(sent_line).decode('utf-8')
postags = postags.strip().split('\n')
for i, el in enumerate(token_list):
if self.is_all_latin(el):
tagged_tuples.append((el, u"FW"))
else:
tagged_tuples.append((el, postags[i]))
return tagged_tuples