def __init__(self, sentence):
en_parser = StanfordParser(
path_to_jar=
'../stanford-parser-full-2018-02-27/stanford-parser.jar',
path_to_models_jar=
'../stanford-parser-full-2018-02-27/stanford-parser-3.9.1-models.jar',
model_path=
'../stanford-parser-full-2018-02-27/stanford-parser-3.9.1-models/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz'
)
sg = StanfordTokenizer(
path_to_jar='../stanford-parser-full-2018-02-27/stanford-parser.jar'
)
self.status = 0
self.trans = googletrans.Translator()
self.sentence = sentence.strip("\n").replace(" ", "")
en_trans = self.trans.translate(sentence).text
en_trans = sg.tokenize(en_trans)
try:
tree = list(en_parser.parse(en_trans))
self.tree = tree[0]
# print(self.tree)
self.rel = []
except:
self.status = 1
def clean_content(page, verbose=True):
content = page.content
tag_name = ''
ret_content = []
for line in content.splitlines():
match = re.match('=+ +(.+)? +=+', line)
# タグかどうか，そうでなければリストに追加
if not match:
if len(line) and not tag_name in clean_content.omit_sections:
ret_content.append(line)
continue
# タグ名更新
tag_name = match.group(1)
match = re.match('(.+)?Edit', tag_name)
if match: tag_name = match.group(1)
# 文頭大文字 文末ピリオド
st = StanfordTokenizer()
ret_tokens = []
for idx, line in enumerate(ret_content):
if verbose:
sys.stdout.write('\rParsing "%s" %d / %d'%(page.title, idx+1, len(ret_content)))
sys.stdout.flush()
tokens = st.tokenize(line)
indices = [0]+[i+1 for i, e in enumerate(tokens) if e in ['.','!','?']]
subtokens = [tokens[indices[i]:indices[i+1]] for i in range(len(indices)-1)]
ret_tokens.extend(filter(lambda tokens: tokens[0][0].isupper(), subtokens))
if verbose: sys.stdout.write('\n'); sys.stdout.flush()
return '\n'.join([' '.join(line) for line in ret_tokens])
def tokenize(content):
"""Breaks up text-based content into tokens in the style of PTB corpus"""
_path_to_jar = os.path.abspath(
'summarize/stanford-postagger/stanford-postagger.jar')
token_list = []
st = StanfordTokenizer(path_to_jar=_path_to_jar)
content = content.lower()
token_list = st.tokenize(content)
return token_list
def __init__(self):
self.dm_single_close_quote = u'\u2019' # unicode
self.dm_double_close_quote = u'\u201d'
self.END_TOKENS = [
'.', '!', '?', '...', "'", "`", '"', self.dm_single_close_quote,
self.dm_double_close_quote, ")"
] # acceptable ways to end a sentence
self.tokenizer = StanfordTokenizer('stanford-postagger.jar',
options={"tokenizeNLs": True})
def tokenize_q(qa, phase):
qas = len(qa)
MyTokenizer = StanfordTokenizer()
for i, row in enumerate((qa)):
row['question_toked'] = MyTokenizer.tokenize(
row['question'].lower())[:14]
if i % 50000 == 0:
json.dump(qa,
open('vqa_' + phase + '_toked_' + str(i) + '.json', 'w'))
if i == qas - 1:
json.dump(qa, open('vqa_' + phase + '_toked.json', 'w'))
def tokenize_stopwords_stemmer(texts):
#用斯坦福的分词采用这一段，用普通分词时不用这个
#tokenize
Str_texts=texts[0]
#tokenizer = StanfordTokenizer(path_to_jar=r"/Users/apple/Documents/tools/stanford-parser-full-2015-04-20/stanford-parser.jar")
tokenizer = StanfordTokenizer(path_to_jar=r"stanford-parser.jar") #path_to_jar: 用来定位jar包，r是防止字符转义的，如果路径中出现'\t'的话 不加r的话\t就会被转义 而加了'r'之后'\t'就能保留原有的样子
java_path = 'C:/Program Files/Java/jdk1.8.0_121/bin/java.exe'
os.environ['JAVAHOME'] = java_path
texts_tokenized=tokenizer.tokenize(Str_texts)#输入必须是字符串,进行分词
#print(texts_tokenized)
p1=r'[-@<#$%^&*].+'
pa1=re.compile(p1) #re.compile()函数，将正则表达式的字符串形式编译为Pattern实例，然后使用Pattern实例处理文本并获得匹配结果（一个Match实例）
texts_filtered0 = [ document for document in texts_tokenized if not document in pa1.findall(document) ]
p2=r'.+[-_\/].+' #将r'.+[-_\./].+'改为r'.+[-_\/].+'，可以保留数字间的句号，比如保留3.1.2这样的格式
pa2=re.compile(p2)
texts_filtered=[]
for document in texts_filtered0:
if document in pa2.findall(document):
if document.find('_')>-1 : #split()：拆分字符串。通过指定分隔符对字符串进行切片，并返回分割后的字符串列表（list）
texts_filtered = texts_filtered + document.split('_')
elif document.find('-')>-1:
texts_filtered = texts_filtered + document.split('-')
elif document.find('.')>-1:
texts_filtered = texts_filtered + document.split('.')
elif document.find('/')>-1:
texts_filtered = texts_filtered + document.split('/')
else:
texts_filtered.append(document)
texts_filtered = [ document for document in texts_filtered if document != '' and document != "''" and document != "``" ]#过滤掉空格，单引号和--
#stopwords
english_stopwords = stopwords.words('english')#得到停词
texts_filtered_stopwords = [ document for document in texts_filtered if not document in english_stopwords]#过滤掉停词
english_punctuations = [',', '.', ':', ';', '?', '(', ')', '[', ']', '&', '!', '*', '@', '#', '$', '%','\n'
,'<','>','/','\"','\'','{','}','!','~','`'
,'$','^','/*','*/','/**','**/','**','-','_','+','=',r'-?-',r'@?']#得到标点
texts_filtered = [ document for document in texts_filtered_stopwords if not document in english_punctuations]#过滤掉标点
#print texts_filtered
temp = texts_filtered[:] #实现去除带'comment'元素的代码
for i in temp:
if 'comment' in i:
texts_filtered.remove(i)
#print(texts_filtered)
#texts_filtered=[re.sub(r'^[1-9]\d*$'.format(punctuation), '', x) for x in texts_filtered] # ^[1-9]\d*$过滤掉整数
porter = nltk.PorterStemmer() #词干提取算法
texts_Stemmered=[porter.stem(t) for t in texts_filtered] #列表类型，提取词干
return texts_Stemmered #返回一个列表
def __init__(self):
# set envirinment variable
# TO DO: update to Docker path
os.environ['CLASSPATH'] = resource_filename(__name__, 'tokenizers/')
# load tokenizer and tagger
# TO DO: again, update to Docker path
self.STANFORD_TOKENIZER = StanfordTokenizer(
resource_filename(__name__, 'tokenizers/stanford-ner-3.6.0.jar'))
self.SMO_tagger = StanfordNERTagger(
resource_filename(__name__,
'classifiers/ner-orgs_2016-03-28_all.ser.gz'))
def spans(txt):
english_tokenizer = StanfordTokenizer(
'C:/Users/dongfangxu9/PycharmProjects/pos_tagger/stanford-postagger.jar',
options={
"americanize": True,
},
java_options='-mx1000m')
tokens = english_tokenizer.tokenize(txt)
offset = 0
for token in tokens:
offset = txt.find(token, offset)
yield token, offset, offset + len(token)
offset += len(token)
def __init__(self):
self.dm_single_close_quote = u'\u2019' # unicode
self.dm_double_close_quote = u'\u201d'
self.END_TOKENS = [
'.', '!', '?', '...', "'", "`", '"', self.dm_single_close_quote,
self.dm_double_close_quote, ")"
] # acceptable ways to end a sentence
# We use these to separate the summary sentences in the .bin datafiles
self.SENTENCE_START = '<s>'
self.SENTENCE_END = '</s>'
self.tokenizer = StanfordTokenizer('stanford-postagger.jar',
options={"tokenizeNLs": True})
def __init__(self, sentence):
en_parser = StanfordParser(path_to_jar='../stanford-corenlp-full-2018-02-27/stanford-corenlp-3.9.1.jar',
path_to_models_jar='../stanford-corenlp-full-2018-02-27/stanford-corenlp-3.9.1-models.jar',
)
sg = StanfordTokenizer(path_to_jar='../stanford-corenlp-full-2018-02-27/stanford-corenlp-3.9.1.jar')
self.trans = googletrans.Translator()
self.sentence = sentence
result1 = sg.tokenize(self.trans.translate(sentence).text)
tree = list(en_parser.parse(result1))
self.tree = tree[0]
self.rel=[]
def stanfordTokenizer ( rawText ):
"""
Uses Stanford University's natural language processing lab
tokenizer to split raw text.
"""
jarPath = "/Users/Nathan/nltk_data/stanford-postagger.jar"
stanfordOptions = {
"americanize": True,
"ptb3Escaping": False
}
stanfordTokenizer = StanfordTokenizer( jarPath, 'UTF-8', stanfordOptions )
return stanfordTokenizer.tokenize( rawText )
def get_sentence_embeddings(sentences, ngram='bigrams', model='concat_wiki_twitter'):
""" Returns a numpy matrix of embeddings for one of the published models. It
handles tokenization and can be given raw sentences.
Arguments:
- ngram: 'unigrams' or 'bigrams'
- model: 'wiki', 'twitter', or 'concat_wiki_twitter'
- sentences: a list of raw sentences ['Once upon a time', 'This is another sentence.', ...]
"""
wiki_embeddings = None
twitter_embbedings = None
tokenized_sentences_NLTK_tweets = None
tokenized_sentences_SNLP = None
if model == "wiki" or model == 'concat_wiki_twitter':
tknzr = StanfordTokenizer(SNLP_TAGGER_JAR, encoding='utf-8')
s = ' <delimiter> '.join(sentences) # just a trick to make things faster
tokenized_sentences_SNLP = tokenize_sentences(tknzr, [s])
tokenized_sentences_SNLP = tokenized_sentences_SNLP[0].split(' <delimiter> ')
assert (len(tokenized_sentences_SNLP) == len(sentences))
wiki_embeddings = get_embeddings_for_preprocessed_sentences(tokenized_sentences_SNLP, \
MODEL_PG_3KBOOKS_BIGRAMS, FASTTEXT_EXEC_PATH)
if model == "wiki":
return wiki_embeddings
elif model == "concat_wiki_twitter":
return np.concatenate((wiki_embeddings, twitter_embbedings), axis=1)
sys.exit(-1)
class tokenizer:
def __init__(self):
self.stanford_tokenizer = \
StanfordTokenizer('../stanford-parser-2010-08-20/stanford-parser.jar'\
,options={"americanize": False})
pass
#tokenize with stanford_parser
def stanford_tokenize(self, row):
temp_list = self.stanford_tokenizer.tokenize(row)
return temp_list
#tokenize with nltk word_tokenizer
def word_tokenize(self, row):
temp_list = nltk.word_tokenize(row)
list_length = len(temp_list)
index_list = list()
for i in xrange(list_length):
if temp_list[i].startswith('\''):
if len(temp_list[i]) > 3:
temp_list[i] = temp_list[i][1:]
index_list.append(i)
#end for
count = 0
for index in index_list:
temp_list.insert(index + count, '\'')
count += 1
#end for
return temp_list
def no_block(self, string):
string = re.sub(r' ', '', string)
return len(string)
class tokenizer:
def __init__(self):
self.stanford_tokenizer = \
StanfordTokenizer('../stanford-parser-2010-08-20/stanford-parser.jar'\
,options={"americanize": False});
pass;
#tokenize with stanford_parser
def stanford_tokenize(self,row):
temp_list = self.stanford_tokenizer.tokenize(row);
return temp_list;
#tokenize with nltk word_tokenizer
def word_tokenize(self,row):
temp_list = nltk.word_tokenize(row);
list_length = len(temp_list);
index_list = list();
for i in xrange(list_length):
if temp_list[i].startswith('\''):
if len(temp_list[i]) > 3:
temp_list[i] = temp_list[i][1:];
index_list.append(i);
#end for
count = 0;
for index in index_list:
temp_list.insert(index+count,'\'');
count+=1;
#end for
return temp_list;
def no_block(self ,string):
string = re.sub(r' ','',string);
return len(string);
def __init__(
self,
singleword_spells,
multiword_spells,
tokenize_by="text", #tokenize_by="sentence",
punkt_tokenizer='tokenizers/punkt/english.pickle',
path_stanford_jar="/home/david/Descargas/stanford-corenlp-3.8.0.jar"
):
self.singleword_spells = singleword_spells
self.multiword_spells = multiword_spells
self.multiword_spells_joint = [
"_".join(s.split()) for s in multiword_spells
]
self.tokenize_by = tokenize_by
self.toktok = StanfordTokenizer(path_to_jar=path_stanford_jar)
self.sent_detector = nltk.data.load(punkt_tokenizer)
def stanfordNERInit():
os.environ[
'CLASSPATH'] = 'C:/users/home/stanford-ner/stanford-ner-2017-06-09/stanford-ner.jar:C:/users/home/stanford-ner/stanford-ner-2017-06-09/lib/*:C:/users/home/stanford-postagger-full-2017-06-09/stanford-postagger.jar'
os.environ[
'STANFORD_MODELS'] = 'C:/users/home/stanford-ner/stanford-ner-2017-06-09/classifiers/'
sent_detection = nltk.data.load('tokenizers/punkt/english.pickle')
st = StanfordNERTagger('english.all.3class.distsim.crf.ser.gz')
tokenizer = StanfordTokenizer()
return sent_detection, st, tokenizer
def __init__(self, data, path, all_names):
self.data = data
self.path = path
self.english_postagger = StanfordPOSTagger(
path + 'models/english-left3words-distsim.tagger',
path + 'lib/stanford-postagger-3.4.1.jar',
java_options='-Xmx2g')
self.english_tokenizer = StanfordTokenizer(
path + 'lib/stanford-postagger-3.4.1.jar', 'utf-8')
self.all_names = all_names
self.pos = self.extract_POS()
self.nms = self.extract_names()
self.wg1 = self.extract_wordgrams(1)
self.wg2 = self.extract_wordgrams(2)
self.cg1 = self.extract_chargrams(1)
self.cg2 = self.extract_chargrams(2)
self.cg3 = self.extract_chargrams(3)
self.bl = self.extract_breaklines()
self.ws = self.extract_websites()
def sentence_embeddings(wikiuni, snlpjar, fasttext, sentences, ngram='unigrams', model='concat_wiki_twitter'):
"""
Generate embeddings from a list of sentences.
Parameters:
-----------
wikiuni: string
Path to the Wikipedia embeddings
parser: string
Path to the folder containing the Stanford Parser
jar: string
Path to the JAR file of the Stanford tagger
fasttext: string
Path to the executable of FastText
sentences: list
List containing raw sentences
e.g., ['Once upon a time', 'This is another sentence.', ...]
ngram: string (unigram|bigram)
ngram used in Wikipedia embeddings
model: string (wiki|twitter|concat_wiki_twitter)
"""
wiki_embeddings = None
twitter_embbedings = None
tokenized_sentences_NLTK_tweets = None
tokenized_sentences_SNLP = None
if model == "wiki" or model == 'concat_wiki_twitter':
tknzr = StanfordTokenizer(snlpjar, encoding='utf-8')
s = ' <delimiter> '.join(sentences) #just a trick to make things faster
tkn_sentences_SNLP = tokenize_sentences(tknzr, [s])
tkn_sentences_SNLP = tkn_sentences_SNLP[0].split(' <delimiter> ')
assert(len(tkn_sentences_SNLP) == len(sentences))
if ngram == 'unigrams':
wiki_embeddings = sent2embeddings(tkn_sentences_SNLP, \
wikiuni, fasttext)
# We are not using Twitter or Bigrams so far
# else:
# wiki_embeddings = sent2embeddings(tkn_sentences_SNLP, \
# MODEL_WIKI_BIGRAMS, FASTTEXT_EXEC_PATH)
# if model == "twitter" or model == 'concat_wiki_twitter':
# tknzr = TweetTokenizer()
# tkn_sentences_NLTK_tweets = tokenize_sentences(tknzr, sentences)
# if ngram == 'unigrams':
# twitter_embbedings = sent2embeddings(tkn_sentences_NLTK_tweets, \
# MODEL_TWITTER_UNIGRAMS, FASTTEXT_EXEC_PATH)
# else:
# twitter_embbedings = sent2embeddings(tkn_sentences_NLTK_tweets, \
# MODEL_TWITTER_BIGRAMS, FASTTEXT_EXEC_PATH)
#
if model == "wiki":
return wiki_embeddings
#elif model == "twitter":
# return twitter_embbedings
#elif model == "concat_wiki_twitter":
# return np.concatenate((wiki_embeddings, twitter_embbedings), axis=1)
sys.exit(-1)
def Tok_handler(self, sentence, parser):
if parser == "spacy":
try:
import spacy, en_core_web_sm
except ImportError:
print("Can't import spacy")
nlp = en_core_web_sm.load()
doc = nlp(sentence)
return [str(token) for token in doc]
elif parser == "nltk":
try:
import nltk
from nltk.tokenize.stanford import StanfordTokenizer
os.environ["CLASSPATH"] = "./StanfordNLP/jars"
os.environ["STANFORD_MODELS"] = "./StanfordNLP/models"
except ImportError:
print("Can't import spacy")
tokenizer = StanfordTokenizer()
return tokenizer.tokenize(sentence)
class Tokenizer(object):
"""
Tokenize sentence
"""
def __init__(self, jar_path):
self.tokenizer = StanfordTokenizer(jar_path)
def tokenize(self, sentence):
return self.tokenizer.tokenize(sentence)
def __call__(self, sentence):
return self.tokenize(sentence)
def __init__(self, sentence):
en_parser = StanfordParser(
path_to_jar=
'../stanford-parser-full-2018-02-27/stanford-parser.jar',
path_to_models_jar=
'../stanford-parser-full-2018-02-27/stanford-parser-3.9.1-models.jar',
model_path=
'../stanford-parser-full-2018-02-27/stanford-parser-3.9.1-models/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz'
)
sg = StanfordTokenizer(
path_to_jar='../stanford-parser-full-2018-02-27/stanford-parser.jar'
)
self.trans = googletrans.Translator()
self.sentence = sentence
result1 = sg.tokenize(self.trans.translate(sentence).get_text())
tree = list(en_parser.parse(result1))
self.tree = tree[0]
self.rel = []
class Preprocessor:
def __init__(self):
self.dm_single_close_quote = u'\u2019' # unicode
self.dm_double_close_quote = u'\u201d'
self.END_TOKENS = [
'.', '!', '?', '...', "'", "`", '"', self.dm_single_close_quote,
self.dm_double_close_quote, ")"
] # acceptable ways to end a sentence
# We use these to separate the summary sentences in the .bin datafiles
self.SENTENCE_START = '<s>'
self.SENTENCE_END = '</s>'
self.tokenizer = StanfordTokenizer('stanford-postagger.jar',
options={"tokenizeNLs": True})
def tokenize(self, article):
return self.tokenizer.tokenize(article)
def fix_missing_period(self, line):
"""Adds a period to a line that is missing a period"""
if line == "": return line
if line[-1] in self.END_TOKENS: return line
# print line[-1]
return line + " ."
def adjust_article(self, article):
#takes the article t
# Lowercase everything
lines = [line.lower() for line in article]
# Put periods on the ends of lines that are missing them (this is a problem in the dataset because many image captions don't end in periods; consequently they end up in the body of the article as run-on sentences)
lines = [self.fix_missing_period(line) for line in lines]
# Separate out article and abstract sentences
article_lines = []
for idx, line in enumerate(lines):
if line == "":
continue # empty line
else:
article_lines.append(line)
# Make article into a single string
article = ' '.join(article_lines)
# # Make abstract into a signle string, putting <s> and </s> tags around the sentences
# abstract = ' '.join(["%s %s %s" % (self.SENTENCE_START, sent, self.SENTENCE_END) for sent in highlights])
return article
def get_sentence_embeddings(sentences, train, d):
""" Returns a numpy matrix of embeddings for one of the published models. It
handles tokenization and can be given raw sentences.
Arguments:
- ngram: 'unigrams' or 'bigrams'
- model: 'wiki', 'twitter', or 'concat_wiki_twitter'
- sentences: a list of raw sentences ['Once upon a time', 'This is another sentence.', ...]
"""
tknzr = StanfordTokenizer(SNLP_TAGGER_JAR, encoding='utf-8')
s = ' <delimiter> '.join(sentences) #just a trick to make things faster
tokenized_sentences_SNLP = tokenize_sentences(tknzr, [s])[0]
# tokenized_sentences_SNLP = tokenized_sentences_SNLP[0].split(' <delimiter> ')
assert (len(tokenized_sentences_SNLP) == len(sentences))
wiki_embeddings = get_embeddings_for_preprocessed_sentences(
tokenized_sentences_SNLP, MODEL_WIKI_UNIGRAMS, FASTTEXT_EXEC_PATH,
train, d)
return wiki_embeddings
class Preprocessor:
def __init__(self):
self.dm_single_close_quote = u'\u2019' # unicode
self.dm_double_close_quote = u'\u201d'
self.END_TOKENS = [
'.', '!', '?', '...', "'", "`", '"', self.dm_single_close_quote,
self.dm_double_close_quote, ")"
] # acceptable ways to end a sentence
self.tokenizer = StanfordTokenizer('stanford-postagger.jar',
options={"tokenizeNLs": True})
def tokenize(self, text):
return self.tokenizer.tokenize(text)
def fix_missing_period(self, line):
"""Adds a period to a line that is missing a period"""
if line == "":
return line
if line[-1] in self.END_TOKENS:
return line
return line + " ."
def preprocess_text(self, text):
"""Preprocesses and prepares input text for summarization"""
# Lowercase everything
lines = [line.lower() for line in text]
lines = [self.fix_missing_period(line) for line in lines]
# Separate out text
text_lines = []
for idx, line in enumerate(lines):
if line == "":
continue # empty line
else:
text_lines.append(line)
# Make text into a single string
text = ' '.join(text_lines)
return text
def __init__(self,mode=None):
self.config = GetConfig()
if mode:
self.mode = mode
else:
if self.config.has_option(self.MY_ID,'mode'):
self.mode = self.config.get(self.MY_ID,'mode')
else:
self.mode = 'NLTK'
if self.mode == 'STANFORD':
from nltk.tokenize.stanford import StanfordTokenizer as Tokenizer
self.tokenizer = Tokenizer()
elif self.mode == 'NLTK':
pass
elif self.mode == 'MINE':
self.spacePunct = re.compile(ur'[`~!@#\$%\^&\*\(\)\[\]{}_\+\-=\|\\:;\"\'<>,\?/]')
self.removePunct = re.compile(ur'\.')
else:
raise Exception('Error: tokenizer, Unknown mode %s!' %(self.mode))
def __init__(self, paths_json):
set_environment_paths(paths_json)
self.sentence_sequences = []
self.valence_sequences = []
self.sentence_trees = []
self.valence_trees = []
self.CompleteWordIndices = []
self.model = ""
self.models2run = []
self.neg_scope_method = ""
self.neg_res_method = ""
self.sent_comp_method = ""
valence_dict_path = paths_json["VALENCE_DICT"]
with open(valence_dict_path) as json_file:
self.VALENCE_DICT = json.loads(json_file.read())
negtool_negscopes_path = paths_json["NEGTOOL_NEGSCOPE"]
self.negtool_neg_scopes_file = open(negtool_negscopes_path, "r")
self.negtool_neg_scopes_file_current_line = 0
self.use_negtool = False
meaning_spec_distribution_dict_path = paths_json[
"MEANING_SPEC_DISTRIBUTION_DICT_PATH"]
with open(meaning_spec_distribution_dict_path) as json_file:
self.distribution_dict = json.loads(json_file.read())
#window neg scope
self.window_size = 4
self.review_id = 0
self.sentence_id = 0 #for negtool purposes
#constants
self.contractions = ["n't", "'m", "'ll", "'d", "'s", "'ve", "'re"]
#parser and tokenizer initialization
# self.PARSER = StanfordParser(model_path="edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz")
self.TOKENIZER = StanfordTokenizer()
#using server
self.CORENLP = StanfordCoreNLP('http://localhost:9000')
class tokenizer(object):
MY_ID = 'TOKENIZER'
def __init__(self,mode=None):
self.config = GetConfig()
if mode:
self.mode = mode
else:
if self.config.has_option(self.MY_ID,'mode'):
self.mode = self.config.get(self.MY_ID,'mode')
else:
self.mode = 'NLTK'
if self.mode == 'STANFORD':
from nltk.tokenize.stanford import StanfordTokenizer as Tokenizer
self.tokenizer = Tokenizer()
elif self.mode == 'NLTK':
pass
elif self.mode == 'MINE':
self.spacePunct = re.compile(ur'[`~!@#\$%\^&\*\(\)\[\]{}_\+\-=\|\\:;\"\'<>,\?/]')
self.removePunct = re.compile(ur'\.')
else:
raise Exception('Error: tokenizer, Unknown mode %s!' %(self.mode))
def tokenize(self, sent):
if sent.endswith('-') or sent.endswith('~'):
sent += ' '
sent = sent.replace('~ ', ' ~ ')
sent = sent.replace('- ', ' - ')
if self.mode == 'STANFORD':
tokens = self.tokenizer.tokenize(sent.strip())
elif self.mode == 'NLTK':
tokens = nltk.word_tokenize(sent.strip())
elif self.mode == 'MINE':
new_sent = sent.strip()
new_sent = self.spacePunct.sub(' ', new_sent)
new_sent = self.removePunct.sub('', new_sent)
tokens = new_sent.split()
p_sent = ' '.join(tokens)
p_sent = p_sent.replace('% ', '%')
p_sent = p_sent.replace('``', '\"')
p_sent = p_sent.replace('\'\'', '\"')
p_tokens = p_sent.split(' ')
return p_tokens
def tokenize(corenlp, review, span=False):
r_dict = corenlp._request('ssplit', review)
tokens2 = StanfordTokenizer().tokenize(review)
print(r_dict)
print(tokens2)
tokens = [
token['word'] for s in r_dict['sentences'] for token in s['tokens']
]
sentences = []
current_sentence = []
for token in tokens:
if (not bool(re.compile(r'[^\!\?]').search(token))
or token == "."): #only ! or ?
current_sentence.append(token)
sentences.append(current_sentence)
current_sentence = []
else:
current_sentence.append(token)
#return [" ".join(sentence[:-1])+sentence[-1] for sentence in sentences] #return sentences
return sentences #return tokenized sentences
def stanford_tokenize(s): return StanfordTokenizer().tokenize(s)
gold = []
for each in lis:
item = each.split("\t")
gold.append(item[1])
item_email = ' '.join(e for e in item[2:])
item_email = item_email.replace('</br>',' ').replace(':',' ')#.split()
#print(item_email)
emails.append(item_email)
emails_len.append(len(item_email))
max_words_email = max(emails_len)
total_email = len(emails)
embedding_size = 700
all_mail_vec = []
tknzr = StanfordTokenizer(SNLP_TAGGER_JAR, encoding='utf-8')
s = ' <delimiter> '.join(emails)
tokenized_sentences_SNLP = tokenize_sentences(tknzr, [s])
emails = tokenized_sentences_SNLP[0].split(' <delimiter> ')
embs_email = model.embed_sentences(emails)
#each_mail_vec = np.zeros((max_words_email, embedding_size))
#print(vector.shape)
f.close()
f = open('emails_dataset/emailExplanations_Dec23.sorted.txt','r')
concepts = {'REMINDER':[], 'HUMOR' : [], 'EVENT': [], 'EMPLOYEE': [], 'MEETING' : [], 'POLICY' : [], 'CONTACT' : []}
import emoji
from nltk.tokenize.stanford import StanfordTokenizer
s = "Good muffins :-X cost $3.88\nin New York. Please buy me\ntwo of them.\nThanks."
tokens = StanfordTokenizer().tokenize(s)
print(tokens)
a = {'a', 'b', 'c'}
b = {'b', 'c', 'd'}
c = {'a', 'd', 'e'}
d = a | b | c
print(d)
reader_obj = csv.reader(csvfile)
for row in reader_obj:
# row[0] -> id
# row[1] -> title
# row[2] -> content
# row[3] -> tags
#soup = BeautifulSoup(row[2])
#if soup.code != None:
# codecount += 1
title_list += " " + row[1]
check = int(time.time() - start)
if check >= 10:
print count, time.time() - start, "seconds"
start = time.time()
if not count % 10000:
word_list = StanfordTokenizer(path_to_jar="/Users/apple/Downloads//stanford-postagger-2015-01-29/stanford-postagger-2015-01-30/stanford-postagger.jar"\
).tokenize(title_list)
fout.write(' '.join(word_list).encode('utf-8') + "\n")
title_list = ""
print(count)
#word_list = nltk.word_tokenize(row[1].encode('utf-8'))
#if not codecount%10000:
# print codecount
count += 1
#print(soup.get_text())
#pdb.set_trace()
word_list = StanfordTokenizer(path_to_jar="/Users/apple/Downloads//stanford-postagger-2015-01-29/stanford-postagger-2015-01-30/stanford-postagger.jar"\
).tokenize(title_list)
fout.write(' '.join(word_list).encode('utf-8') + "\n")
title_list = []
print(count)
print("Tokenizing all requests.")
tweet_tokenizer = TweetTokenizer(preserve_case=True,
reduce_len=True,
strip_handles=True)
tokenized_datasets_original_tweet = [[
tweet_tokenizer.tokenize(request) for request in dataset
] for dataset in datasets]
print("Retokenizing with Stanford tokenizer. This may take a long time.")
path_pos = "/playpen/home/tongn/stanford-postagger-full-2017-06-09/"
jar_pos = "stanford-postagger.jar"
tokenizer = StanfordTokenizer(path_pos + jar_pos)
tokenizer = StanfordTokenizer(tagger_path)
# tokenized_datasets_original = [
# [tokenizer.tokenize(' '.join(request).strip())
# for request in dataset]
# for dataset in tokenized_datasets_original_tweet]
tokenized_datasets_original = tokenized_datasets_original_tweet
"""
Convert all tokens to lowercase
"""
tokenized_datasets = [[[token.lower() for token in request]
for request in dataset]
for dataset in tokenized_datasets_original]
"""
Build the whole vocabulary
def test_stanford_tokenizer():
files = os.listdir("/Users/ruben/Desktop/txt/")
standfor = StanfordTokenizer()
total = sum(len(standfor.tokenize(readfile(DOCS_TXT_ROOT + f))) for f in files)
print "\nStanfordTokenizer total " + str(total)
class Values(object):
def __init__(self, data, path, all_names):
self.data = data
self.path = path
self.english_postagger = StanfordPOSTagger(
path + 'models/english-left3words-distsim.tagger',
path + 'lib/stanford-postagger-3.4.1.jar',
java_options='-Xmx2g')
self.english_tokenizer = StanfordTokenizer(
path + 'lib/stanford-postagger-3.4.1.jar', 'utf-8')
self.all_names = all_names
self.pos = self.extract_POS()
self.nms = self.extract_names()
self.wg1 = self.extract_wordgrams(1)
self.wg2 = self.extract_wordgrams(2)
self.cg1 = self.extract_chargrams(1)
self.cg2 = self.extract_chargrams(2)
self.cg3 = self.extract_chargrams(3)
self.bl = self.extract_breaklines()
self.ws = self.extract_websites()
def getVals(self):
return self.bl, self.wg1, self.wg2, self.ws, self.nms, self.pos, self.cg1, self.cg2, self.cg3
def extract_POS(self):
return self.english_postagger.tag(
self.english_tokenizer.tokenize(self.data))
def extract_websites(self):
websites = []
result = re.findall('href=\"(.*?)\"', self.data)
for r in result:
if (r == 'mailto:') or (r == 'http:///'): continue
else: websites.append(r)
return websites
def extract_breaklines(self):
breaklines = []
idx_old = 0
idx_new = self.data.find('<br>')
breaklines.append(idx_new - idx_old)
idx_old = idx_new
while idx_old < len(self.data):
idx_new = self.data.find('<br>', idx_old + 4)
if (idx_new == -1): break
breaklines.append(idx_new - idx_old)
idx_old = idx_new
return breaklines
def extract_chargrams(self, gram_size):
return [
''.join(self.data[i:i + gram_size])
for i in range(len(self.data) - gram_size + 1)
]
def extract_wordgrams(self, gram_size):
r = re.compile(r'[\s{}\t\n\r\+\>\<\=\¢\â\$]+'.format(
re.escape(punctuation)))
word_list = r.split(self.data)
#word_list = re.split('\W+', self.data)
#word_list = re.split(r'[\p{P} \\t\\n\\r\\+\\>\\<\\=\\¢\\â\\$]+', self.data)
word_list = filter(None, word_list)
return [
''.join(word_list[i:i + gram_size])
for i in range(len(word_list) - gram_size + 1)
]
def extract_names(self):
r = re.compile(r'[\s{}\t\n\r\+\>\<\=\¢\â\$]+'.format(
re.escape(punctuation)))
word_list = r.split(self.data)
#word_list = re.split('\W+', self.data)
#word_list = re.split('[\p{P} \\t\\n\\r\\+\\>\\<\\=\\¢\\â\\$]+', self.data)
word_list = filter(None, word_list)
word_list = [x.lower() for x in word_list]
return list(set(word_list) & set(self.all_names))
#sqlite3 connection
dbname = '/home/aahu/Dropbox/ryancompton.net/assets/praw_drugs/drugs.db'
conn = sqlalchemy.create_engine('sqlite+pysqlite:///' + dbname,
module=sqlite3.dbapi2)
def load_subreddit(tablename, conn):
df = pd.read_sql(tablename, conn)
return df
# <codecell>
from nltk.tokenize.stanford import StanfordTokenizer
stanfordTokenizer = StanfordTokenizer(
path_to_jar=
'/home/aahu/Downloads/stanford-corenlp-full-2015-01-30/stanford-corenlp-3.5.1.jar'
)
def my_tokenize(text):
return nltk.word_tokenize(text)
#return nltk.wordpunct_tokenize(text)
#return stanfordTokenizer.tokenize(text)
#return nltk.tokenize.TreebankWordTokenizer().tokenize(text)
def build_tfidf_transformer(docs=[],
tokenizer=my_tokenize,
max_doc_count=2000,
vocab_limit=10000):
"""
def __init__(self):
self.stanford_tokenizer = \
StanfordTokenizer('../stanford-parser-2010-08-20/stanford-parser.jar'\
,options={"americanize": False});
pass;
