def part_of_speeach(self):
os.environ["JAVA_HOME"] = "/usr/bin/java"
jar = '/home/ahmad/PycharmProjects/untitled1/stanford-postagger-full-2015-12-09/stanford-postagger.jar'
model = '/home/ahmad/PycharmProjects/untitled1/stanford-postagger-full-2015-12-09/models/arabic.tagger'
# model = '/home/ahmad/PycharmProjects/untitled1/stanford-postagger-2011-04-20/models/left3words-wasj-0-18.tagger'
tagger = StanfordPOSTagger(model, jar)
tagger.java_options = '-mx4096m' ### Setting higher memory limit for long sentences
text = tagger.tag(word_tokenize(self.lyrics[0]))
s = ''
for i in text:
f = i[0] + ' ' + i[1]
s = s + f + ' / '
return s
def postagger_nltk(word_lists):
os.environ['JAVAHOME'] = JAVA_PATH
os.environ["STANFORD_PARSER"] = STANFORD_POSTAGGER_PATH
os.environ["STANFORD_MODELS"] = STANFORD_POSTAGGER_MODELS
chinese_tagger = StanfordPOSTagger(model_filename=nltk_pos_model_filename,
path_to_jar=STANFORD_POSTAGGER_PATH)
chinese_tagger.java_options = '-mx12000m'
nltk_all_tag = []
flag = 1
for sentence in word_lists:
analyse = chinese_tagger.tag(sentence.split())
str_tag = ""
for tag_tuple in analyse:
tag_list = [tag_char for tag_char in tag_tuple[1].split("#")]
str_tag += tag_list[0] + "/" + tag_list[1] + " "
nltk_all_tag.append(str_tag.strip())
print("######LTP POSTagger finished " + str(flag) + " sentences")
flag += 1
print("NLTK POATagger is finished!!")
return nltk_all_tag
def __init__(self, file_path, tagged_words_path=None):
'''Creates a Collocations instance with a text
file_path - string path to .txt input file; used to generate full
description of results in output file, whether or not tagged_words
is given
tagged_words_path - string path to .txt file containing string
representation of list of tagged words in input file; saves time and
resources on computation
'''
self.file_path = file_path
if tagged_words_path == None:
#open input file, extract text, and close file
document = open(file_path, 'r', encoding='utf-8')
raw = document.read().lower()
document.close()
#tokenize text into words and tag parts of speech using the
#Stanford part-of-speech tagger
sentences = nltk.sent_tokenize(raw)
tokenized_sentences = [nltk.word_tokenize(w) for w in sentences]
java_path = 'C:/Program Files/Java/jdk-9.0.1/bin/java.exe'
os.environ['JAVAHOME'] = java_path
path_to_model = ('stanford-postagger-2017-06-09/models/'
'english-left3words-distsim.tagger')
path_to_jar = ('stanford-postagger-2017-06-09/'
'stanford-postagger.jar')
tagger = StanfordPOSTagger(path_to_model, path_to_jar)
tagger.java_options='-mx4096m'
tagged_sentences = tagger.tag_sents(tokenized_sentences)
self.tagged_words = sum(tagged_sentences, [])
else:
#load pre-tagged words
import ast
document = open(tagged_words_path, 'r', encoding='utf-8')
self.tagged_words = ast.literal_eval(document.read())
document.close()
from firebase_admin import credentials, firestore
cred = credentials.Certificate(
"../DjangoBackendOne/news/newsapp-54f7c-firebase-adminsdk-wzja4-dc085fad0b.json"
)
firebase_admin.initialize_app(cred)
db = firestore.client()
jar = '../DjangoBackendOne/stanford-postagger-2018-10-16/stanford-postagger.jar'
model = '../DjangoBackendOne/stanford-postagger-2018-10-16/models/english-left3words-distsim.tagger'
java_path = "C:/Program Files/Java/jdk1.8.0_101/bin/java.exe"
os.environ['JAVAHOME'] = java_path
nltk.internals.config_java('C:/Program Files/Java/jdk1.8.0_101/bin/java.exe')
pos_tagger = StanfordPOSTagger(model, jar)
pos_tagger.java_options = '-mx4096m'
config = {
"apiKey": "AIzaSyBJumddViT3Y70F6vmEdP_1VMGXqEFaqgg",
"authDomain": "newsapp-54f7c.firebaseapp.com",
"databaseURL": "https://newsapp-54f7c.firebaseio.com",
"projectId": "newsapp-54f7c",
"storageBucket": "newsapp-54f7c.appspot.com",
"messagingSenderId": "841850292385"
}
# firebase = pyrebase.initialize_app(config)
newsObjects = []
entityCount = 1
from nltk import word_tokenize
import os
# java_path = "/usr/local/java"
# os.environ['JAVAHOME'] = java_path
# os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-7-oracle-cloudera/jre"
os.environ["JAVA_HOME"] = "/usr/bin/java"
# Alternatively to setting the CLASSPATH add the jar and model via their path:
jar = '/home/ahmad/PycharmProjects/untitled1/stanford-postagger-2011-04-20/stanford-postagger.jar'
#model = 'D:\ArabNLP\stanford-postagger-2015-12-09\models\english-left3words-distsim.tagger'
model = '/home/ahmad/PycharmProjects/untitled1/stanford-postagger-2011-04-20/models/left3words-wsj-0-18.tagger'
# model = '/home/ahmad/PycharmProjects/untitled1/english-left3words-distsim.tagger'
tagger = StanfordPOSTagger(model, jar)
tagger.java_options = '-mx4096m' ### Setting higher memory limit for long sentences
text = tagger.tag(word_tokenize(u'أنا تسلقت شجرة'))
s = ''
for i in text:
f = i[0] + ' ' + i[1]
s = s + f + ' / '
# print(i)
# as_list = u"['" + u"', '".join(i) + u"']"+as_list
print s
#print(text)
