def frequencyGenerator(self, s):
pat = '[0-9|.| |\-|\[|\]|-|!|,|\\n|\\|/|:|"|(|)|=|<|>|@|\'|#]'
tokenizer= RegexpTokenizer(pat,gaps=True)
allWords = tokenizer.tokenize(s)
#stemmer = WordnetStemmer()
allWords = map(lambda x:x.lower().strip(),allWords)
#allWordsStemmed = map(lambda x: stemmer.lemmatize(x),allWords)
#del(allWords)
allWords = self.cleanedWords(allWords)
allWordsStemmed = allWords
allWordsStemmed = filter(lambda x:len(x)>2,allWordsStemmed)
#allWordsStemmed = filter(lambda x:len(x)>2,map(lambda x: stemmer.lemmatize(x),allWords))
dic={}
for i in allWordsStemmed:
if dic.has_key(i):
dic[i] = dic[i]+1
else:
dic[i]= 1
st=''
dic=sorted(dic.items(), key=lambda(k,v):(v,k),reverse=True)
for k in dic:
try:
st+=str(k[0])+','+str(k[1])+','
except:
pass
print st
def parse(self, fname):
"""
Парсинг текста файла
:param fname: имя файла
:return: (<имя_файла>, тошнота, мошенничество)
"""
density, fraud = 0, 0
with codecs.open(fname, "r", encoding="utf-8") as f:
text = f.read()
tknz = RegexpTokenizer(pattern="[А-Яа-яA-zё]+")
txt_list = tknz.tokenize(text)
if txt_list:
for i, word in enumerate(txt_list):
new_word = self.check_word(word)
if new_word:
txt_list[i] = new_word
fraud += 1
txt_list = [
word.lower() for word in txt_list
if not (word.lower() in self.sw)
]
stemmer_ru = RussianStemmer()
txt_list = [
stemmer_ru.stem(token.lower()) for token in txt_list
if len(token) > 1
]
dict_w = Counter(txt_list)
top5 = heapq.nlargest(5, dict_w, key=dict_w.get)
top5_count = sum([dict_w[word] for word in top5])
density = top5_count / len(txt_list)
# такой критерий (fraud > 2) был выбран на основании тестирования на имеющейся выборке
# часто попадается такое, что в объявлении есть слова типа "ШxДхВ" которые мы не можем однозначно распознать
# готов обсуждать этот критерий, возможно исправить каким то образом
return fname, density, fraud > 2
def tokenize(text):
#This regex is edited to accept character words only.
str_ = "[A-Za-z]+"
regex_tokens = RegexpTokenizer(str_)
tokens = regex_tokens.tokenize(text.lower())
stems = stem_tokens(tokens, WNL)
return stems
def tokenize_sentence(text, preprocess=True):
'''
Tokenize the given sentence and applies preprocessing if requested
(conversion to lower case and digit substitution).
'''
if preprocess:
text = re.sub(r'\d', '9', text.lower())
tokenizer_regexp = ur'''(?ux)
([^\W\d_]\.)+| # one letter abbreviations, e.g. E.U.A.
\d{1,3}(\.\d{3})*(,\d+)| # numbers in format 999.999.999,99999
\d{1,3}(,\d{3})*(\.\d+)| # numbers in format 999,999,999.99999
\d+:\d+| # time and proportions
\d+([-\\/]\d+)*| # dates. 12/03/2012 12-03-2012
[DSds][Rr][Aa]?\.| # common abbreviations such as dr., sr., sra., dra.
[Mm]\.?[Ss][Cc]\.?| # M.Sc. with or without capitalization and dots
[Pp][Hh]\.?[Dd]\.?| # Same for Ph.D.
[^\W\d_]{1,2}\$| # currency
(?:(?<=\s)|^)[\#@]\w*[A-Za-z_]+\w*| # Hashtags and twitter user names
-[^\W\d_]+| # clitic pronouns with leading hyphen
\w+([-']\w+)*| # words with hyphens or apostrophes, e.g. não-verbal, McDonald's
-+| # any sequence of dashes
\.{3,}| # ellipsis or sequences of dots
\S # any non-space character
'''
tokenizer = RegexpTokenizer(tokenizer_regexp)
return tokenizer.tokenize(text)
def tokenize(self, text):
"""
tokenize text into a list of Token objects
:param text: text to be tokenized (might contains several sentences)
:type text: str
:return: List of Token objects
:rtype: list(Token)
"""
tokens = []
if self.tokenizer_type == "SpaceTokenizer":
operator = RegexpTokenizer('\w+|\$[\d\.]+|\S+')
for counter, span in enumerate(operator.span_tokenize(text)):
new_token = Token(counter, text[span[0]:span[1]], span[0], span[1])
tokens.append(new_token)
elif self.tokenizer_type == "NLTKWhiteSpaceTokenizer":
operator = WhitespaceTokenizer()
for counter, span in enumerate(operator.span_tokenize(text)):
new_token = Token(counter, text[span[0]:span[1]], span[0], span[1])
tokens.append(new_token)
elif self.tokenizer_type == "PTBTokenizer":
ptb_tokens = word_tokenize(text)
counter = 0
for token, span in self._penn_treebank_tokens_with_spans(text, ptb_tokens):
new_token = Token(counter, token, span[0], span[1])
counter += 1
tokens.append(new_token)
return tokens
def get_emails_sent_by_person_list(emails_df):
tokenizer = RegexpTokenizer(r'(?u)\b\w\w+\b')
emails_df['subject_wc'] = emails_df['subject'].map(
lambda x: len(tokenizer.tokenize(x)))
emails_df['content_wc'] = emails_df['content'].map(
lambda x: len(tokenizer.tokenize(x)))
grouped_by_people = emails_df.groupby('from').agg({
'content': 'count',
'subject_wc': 'mean',
'content_wc': 'mean',
})
grouped_by_people.rename(columns={
'content': 'N emails',
'subject_wc': 'Subject word count',
'content_wc': 'Content word count'
},
inplace=True)
grouped_by_people.sort_values(by=['N emails'], ascending=False)
file_path_send = file_path = os.path.join(dir_path,
'results/emails_by_person.csv')
grouped_by_people.to_csv(file_path_send)
def token_words(lyric):
"""
in: lyric(element of row['text'])
take whole lyric and convert it into list of words for analysis
apply few cleaning processes tot remove punctuation & stopwords & errors(Minor focus on this)
return: list of words in the lyric
"""
lyric = lyric.lower()
"""
tokenizer that will tokenize lyric('text') into words without punctuation
it will split aphostrophe words into 2 seperate words but its okay
as most of the time words with aphostrophe are non-main verbs(would,should,etc)
non-main verbs are usually insignificant in most of the context and will be deleted
e.g : would've = would ve but this is fine as we know stopwords will remove ve
tweetTokenizer was producing very irregular words in lyric such as (8, numbers and was dist
"""
#apply tokenizer
tokenizer1 = RegexpTokenizer("[a-z]+")
words = tokenizer1.tokenize(lyric)
#convert list of stopwords to set of stopwords for faster access
en_stopwords = set(stopwords.words('english'))
#we remove stopwords in words
#and add few words that were in the words_lyric for cleaner process
en_stopwords.add('chorus')
#single letters aren't really words :)
for c in ascii_lowercase:
en_stopwords.add(c)
words_lyric = [w for w in words if not w in en_stopwords]
#postProcess of words_lyric
words_lyric = postProcess(words_lyric)
return words_lyric
def no_stop_tokens(self,text):
tokens = []
tokenizer = RegexpTokenizer('(\$?\d+\.\d+)|(([\w]+-)*[\w]+)')
tokens += tokenizer.tokenize(text)
#stemmer = nltk.stem.snowball.EnglishStemmer()
#tokens = map(lambda x: stemmer.stem(x),tokens)
return tokens
def tokenize_sentence(text, preprocess=True):
'''
Tokenize the given sentence and applies preprocessing if requested
(conversion to lower case and digit substitution).
'''
if preprocess:
text = re.sub(r'\d', '9', text.lower())
tokenizer_regexp = ur'''(?ux)
([^\W\d_]\.)+| # one letter abbreviations, e.g. E.U.A.
\d{1,3}(\.\d{3})*(,\d+)| # numbers in format 999.999.999,99999
\d{1,3}(,\d{3})*(\.\d+)| # numbers in format 999,999,999.99999
\d+:\d+| # time and proportions
\d+([-\\/]\d+)*| # dates. 12/03/2012 12-03-2012
[DSds][Rr][Aa]?\.| # common abbreviations such as dr., sr., sra., dra.
[Mm]\.?[Ss][Cc]\.?| # M.Sc. with or without capitalization and dots
[Pp][Hh]\.?[Dd]\.?| # Same for Ph.D.
[^\W\d_]{1,2}\$| # currency
(?:(?<=\s)|^)[\#@]\w*[A-Za-z_]+\w*| # Hashtags and twitter user names
-[^\W\d_]+| # clitic pronouns with leading hyphen
\w+([-']\w+)*| # words with hyphens or apostrophes, e.g. não-verbal, McDonald's
-+| # any sequence of dashes
\.{3,}| # ellipsis or sequences of dots
\S # any non-space character
'''
tokenizer = RegexpTokenizer(tokenizer_regexp)
return tokenizer.tokenize(text)
def create_sents(toks):
wordre = r"\w+@[\w.]+|'s|[0-9]+[.0-9]+|[0-9]+|^\w+:|([A-Z][.]+)+|(\w+[-']?)+|[.!?]|:\w*\n"
s = RegexpTokenizer(wordre).tokenize(toks)
wordre = r"\w+@[\w.]+|'s|[0-9]+[.0-9]+|[0-9]+|^\w+:|([A-Z][.]+)+|(\w+[-']?)+|[.!?]|:\w*\n"
for sentence in s:
RegexpTokenizer(wordre).tokenize(sentence)
return toks
def __init__(self):
self.tokenize=RegexpTokenizer(r'\b([A-Za-z]+)\b') #remove the punctuations
if ver==2:
self.stemmer = SnowballStemmer("english") #using stemmed version of words
elif ver==1:
self.stemmer = LancasterStemmer()
else:
self.stemmer = PorterStemmer()
def tokenize(self,text):
tokens = []
wordnet_lemmatizer = WordNetLemmatizer()
tokenizer = RegexpTokenizer('(\$?\d+\.\d+)|(([\w]+-)*[\w]+)')
tokens += tokenizer.tokenize(text)
tokens = filter(lambda x: x.lower() not in STOP_WORDS and len(x) >1 ,tokens)
tokens = map(lambda token: wordnet_lemmatizer.lemmatize(token), tokens)
return tokens
def readreviews(filename,header=True, fieldsep="\t"):
'''
Reads traing file for LDA
Parameters:
- filename: input filename
- header: True if the header line is present; False otherwise.
- fieldsep: separator
Return:
- a list of entry (where each entry is a tuple of ID, list of tokens, sale/nosale).
'''
review_data = list()
prevsid = ""
#filehandle = open(filename, "r")
stopwords = loadstopwords("english.stop.txt")
pdsnotthere=0
title_notthere=0
tokenizer = RegexpTokenizer('[a-z]\w+')
with open(filename, 'rU') as filename:
filehandle = csv.reader(filename,delimiter='\t', quotechar='"')
#print filehandle
for line in filehandle:
#lineparts = line.split(fieldsep)
fields=line
if header:
header=False
continue
if len(fields)!=12:
continue
print len(fields)
"""
print len(fields)
print fields
"""
HotelID=fields[0]
hotelname=fields[1]
HotelURL=fields[2]
Address=fields[3]
ImgURL=fields[4]
Author=fields[5]
Price=fields[6]
location=fields[7]
Title=fields[8]
ReviewID=fields[9]
Content=fields[10]
Rating_ovarall=fields[11]
review_content=Title+' '+Content
review_discription=review_content
words = [ token for token in tokenizer.tokenize(review_discription) if token not in stopwords ]
review_data.append( words)
return review_data
def tokenize(self,text):
tokens = []
tokenizer = RegexpTokenizer('(\$?\d+\.\d+)|(([\w]+-)*[\w]+)')
#tokens += tokenizer.tokenize(self.title.lower())
tokens += tokenizer.tokenize(text.lower())
tokens = filter(lambda x: x not in STOP_WORDS and len(x) >1 ,tokens)
#stemmer = nltk.stem.snowball.EnglishStemmer()
#tokens = map(lambda x: stemmer.stem(x),tokens)
return tokens
def emailExtractor(sentence, word):
# https://stackoverflow.com/questions/39777806/how-to-update-nltk-package-so-that-it-does-not-break-email-into-3-different-toke
pattern = r'\S+@[^\s.]+\.[a-zA-Z]+|\w+|[^\w\s]'
tokeniser = RegexpTokenizer(pattern)
for w in tokeniser.tokenize(sentence):
if re.search('^(\w|\.|\_|\-)+[@](\w|\_|\-|\.)+[.]\w{2,3}$', w):
context["email"] = w
return True
return False
def __init__(self, args, opts):
super(Method, self).__init__(args, opts)
self.es_int = ESInterface(host=self.opts.server,
port=self.opts.port,
index_name=self.opts.index_name)
self.analyzer = self.es_int.get_index_analyzer()
self.regex_citation = re.compile(
r"\(\s?(([A-Za-z\-]+\s)+([A-Za-z\-]+\.?)?,?\s\d{2,4}(;\s)?)+\s?\)|"
r"(\[(\d+([,–]\s?)?)+\])|"
r"\[[\d,-]+\]").sub
self.all_digits = re.compile(r"^\d+$").search
if self.opts.remove_stopwords:
with file(self.opts.stopwords_path) as f:
self.stopwords = frozenset([l.strip().lower() for l in f])
else:
self.stopwords = frozenset([])
self.db = MySQLdb.connect(host=constants.mysql_server,
port=constants.mysql_port,
user=constants.mysql_user,
passwd=constants.mysql_pass,
db=constants.mysql_db)
self.cur = self.db.cursor()
self.ttys = ['SY']
ttygroups = {
"syns": ('AUN', 'EQ', 'SYN', 'MTH'),
"chemicals": ('CCN', 'CSN'),
"drugs": ('BD', 'BN', 'CD', 'DP', 'FBD', 'GN', 'OCD'),
"diseases": ('DI', ),
"findings": ('FI', ),
"hierarchy": ('HS', 'HT', 'HX'),
"related": ('RT', ),
"preferred": ('PTN', 'PT')
}
self.doc_mod = documents_model.DocumentsModel(opts.anns_dir)
# self.ann_client = AnnotationsClient()
self.reg_apa = re.compile(
# [Chen et al.2000]
r"\(\s?(([A-Za-z\-]+\s)+([A-Za-z\-]+\.?)?,?\s\d{2,4}(;\s)?)+\s?\)|"
r"\(\s?([^ ]+\s?[^ ]*et\sal\.,?\s\d{2,4}(,\s)?)+(\sand\s)?[^ ]+\s?[^ ]*et\sal\.,?\s\d{2,4}\)|"
r"\w+\set al\. \(\d{2,4}\)") # [Chen et al. 200]
self.reg_apa_rare = re.compile(
r"((([A-Z]\w+\set\sal\.,? \d{4})|([A-Z]\w+\sand\s[A-Z]\w+,?\s\d{4}))((,\s)| and )?)+"
)
self.reg_apa2 = re.compile(
r"\(\s?(\w+\s?\w*et\sal\.,\s\d{2,4}(,\s)?)+(\sand\s)?\w+\s?\w*et\sal\.,\s\d{2,4}\)"
)
self.reg_ieee = re.compile(r"(\[(\d+([,–]\s?)?)+\])|\[\s?[\d,-]+\]")
self.reg_paranthesis = re.compile(
r"\(\s?\d{1,2}(,\s\d{1,2})*(\sand\s\d{1,2})?\)")
self.nlp_extractor = Extract_NLP_Tags()
self.tokenizer = RegexpTokenizer('[^\w\-\']+', gaps=True)
self.lmtzr = WordNetLemmatizer()
self.stemmer = stem.porter.PorterStemmer()
def __init__(self):
nltk.download("punkt")
nltk.download("stopwords")
nltk.download("wordnet") # lemmatization
self._tokenizer = RegexpTokenizer(r"\w+")
self._stop_words = set(stopwords.words("english"))
# self._stemmer = nltk.stem.SnowballStemmer("english")
self._lemmatizer = nltk.wordnet.WordNetLemmatizer()
self._vocabulary = set()
def tokenize_large_text_file(file_name, file_location):
tokens_array = set()
file = os.path.join(file_location, file_name)
with open(file,'r') as file:
for line in file:
tokenizer = RegexpTokenizer('\s+', gaps=True)
tokens_array.update(tokenizer.tokenize(line))
tokens_dict = {str(file_name) + " - {} tokens".format(file_name): list(tokens_array)}
with open('tokens taken from - {} - .json'.format(str(file_name)), 'w') as f:
json.dump(tokens_dict, f)
def calcLocation(self, token):
len = self.instTab.searchFormat(token.operator)
if len > 0:
return len
else:
if token.operator == "RESW" or token.operator == "WORD":
len = 3
elif token.operator == "RESB":
len = int(token.operand[0])
elif token.operator == "BYTE":
len = 1
elif token.operator == "LTORG":
len = self.literalTab.literalCount
self.literalTab.setLiteralCount(0)
count = 0
for litCheck in self.literCheck:
if litCheck[1:2] == 'C':
len = 3
else:
len = 1
self.literalTab.modifyLiteral(
litCheck, TokenTable.locCount + (count * len))
count += 1
elif token.operator == "END":
len = self.literalTab.literalCount
self.literalTab.setLiteralCount(0)
count = 0
for litCheck in self.literCheck:
self.literalTab.modifyLiteral(litCheck, token.location)
count += 1
elif token.operator == "EQU":
if token.operand[0] == "*":
len = 0
else:
tokenizer = RegexpTokenizer("-", gaps=True)
tokens = tokenizer.tokenize(token.operand[0])
value1 = self.symTab.search(tokens[0])
value2 = self.symTab.search(tokens[1])
len = value1 - value2
self.symTab.modifySymbol(token.label, len)
len = 0
else:
len = -1
return len
def getDoc_set():
tokenizer = RegexpTokenizer(r'\w+')
for doc in getCorpus.corpus_doc:
# print type(doc)
raw = doc.lower()
tokens = tokenizer.tokenize(raw)
en_stop = get_stop_words("en")
stopped_tokens = [i for i in tokens if i not in en_stop]
p_stemmer = PorterStemmer()
texts = [p_stemmer.stem(i).encode('utf-8') for i in stopped_tokens]
getCorpus.doc_set.append(texts)
def get_search_terms(request: HttpRequest):
# Get any search terms
tr = RegexpTokenizer('[^"\s]\S*|".+?"', gaps=False)
search_text = str(request.GET.get('search_text', ''))
# Respect quoted strings
search_terms = tr.tokenize(search_text)
if len(search_terms) == 0:
solr_search_terms = "*"
else:
solr_search_terms = ' '.join(search_terms)
return solr_search_terms
def get_search_terms(search_text: str):
# Get any search terms
tr = RegexpTokenizer('[^"\s]\S*|".+?"', gaps=False)
# Respect quoted strings
search_terms = tr.tokenize(search_text)
if len(search_terms) == 0:
solr_search_terms = "*"
else:
solr_search_terms = ' '.join(search_terms)
return solr_search_terms
def __init__(self, corpus, tokenize_str, delimiter, n, max_length):
self.corpus = corpus
self.tokenizer = RegexpTokenizer(tokenize_str)
self.delimiter = delimiter
self.n = n
self.max_length = max_length
# use set methods to set these variables
self.tokenized_corpus = []
self.startList = []
self.ngramDict = defaultdict(list)
self.unigramDict = defaultdict(list)
self.set_tokenized_corpus()
self.set_ngrams()
def sentence_length(corpus):
too_long_sentences = []
total_sentences = 0
tokenizer = RegexpTokenizer("\s+", gaps=True)
articles = preprocessing(corpus)
for article in articles:
sentences = sent_tokenize(article)
total_sentences += len(sentences)
for sentence in sentences:
words = tokenizer.tokenize(sentence)
if (len(words) > 25):
too_long_sentences.append((sentence, len(words)))
return (1 - len(too_long_sentences) / total_sentences) * 100
def __init__(self, args, opts):
super(Method, self).__init__(args, opts)
self.es_int = ESInterface(host=self.opts.server,
port=self.opts.port,
index_name=self.opts.index_name)
self.regex_citation = re.compile(r"\(\s?(([A-Za-z\-]+\s)+([A-Za-z\-]+\.?)?,?\s\d{2,4}(;\s)?)+\s?\)|"
r"(\[(\d+([,–]\s?)?)+\])|"
r"\[[\d,-]+\]").sub
self.all_digits = re.compile(r"^\d+$").search
if self.opts.remove_stopwords:
with file(self.opts.stopwords_path) as f:
self.stopwords = frozenset([l.strip().lower() for l in f])
else:
self.stopwords = frozenset([])
self.doc_mod = documents_model.DocumentsModel(opts.docs_path)
self.ann_client = AnnotationsClient()
self.reg_apa = re.compile(
# [Chen et al.2000]
r"\(\s?(([A-Za-z\-]+\s)+([A-Za-z\-]+\.?)?,?\s\d{2,4}(;\s)?)+\s?\)|"
r"\(\s?([^ ]+\s?[^ ]*et\sal\.,?\s\d{2,4}(,\s)?)+(\sand\s)?[^ ]+\s?[^ ]*et\sal\.,?\s\d{2,4}\)|"
r"\w+\set al\. \(\d{2,4}\)") # [Chen et al. 200]
self.reg_apa_rare = re.compile(
r"((([A-Z]\w+\set\sal\.,? \d{4})|([A-Z]\w+\sand\s[A-Z]\w+,?\s\d{4}))((,\s)| and )?)+")
self.reg_apa2 = re.compile(
r"\(\s?(\w+\s?\w*et\sal\.,\s\d{2,4}(,\s)?)+(\sand\s)?\w+\s?\w*et\sal\.,\s\d{2,4}\)")
self.reg_ieee = re.compile(r"(\[(\d+([,–]\s?)?)+\])|\[\s?[\d,-]+\]")
self.reg_paranthesis = re.compile(
r"\(\s?\d{1,2}(,\s\d{1,2})*(\sand\s\d{1,2})?\)")
self.nlp_extractor = Extract_NLP_Tags()
self.tokenizer = RegexpTokenizer('[^\w\-\']+', gaps=True)
self.lmtzr = WordNetLemmatizer()
def getNew_object(docs, newDoc_object):
tokenizer = RegexpTokenizer(r'\w+')
for doc in docs:
# print type(doc[1])
raw = doc[1].lower()
tokens = tokenizer.tokenize(raw)
en_stop = get_stop_words("en")
stopped_tokens = [i for i in tokens if i not in en_stop]
p_stemmer = PorterStemmer()
texts = {}
# texts = [p_stemmer.stem(i).encode('utf-8') for i in stopped_tokens]
# print texts
for i in stopped_tokens:
texts[p_stemmer.stem(i).encode('utf-8')] = texts.get(
p_stemmer.stem(i).encode('utf-8'), 0) + 1
newDoc_object.append(texts)
def __init__(self):
tokenizer_regexp = r'''(?ux)
# the order of the patterns is important!!
# more structured patterns come first
[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+(?:\.[a-zA-Z0-9-]+)+| # emails
(?:https?://)?\w{2,}(?:\.\w{2,})+(?:/\w+)*| # URLs
(?:[\#@]\w+)| # Hashtags and twitter user names
(?:[^\W\d_]\.)+| # one letter abbreviations, e.g. E.U.A.
(?:[DSds][Rr][Aa]?)\.| # common abbreviations such as dr., sr., sra., dra.
(?:\B-)?\d+(?:[:.,]\d+)*(?:-?\w)*|
# numbers in format 999.999.999,999, possibly followed by hyphen and alphanumerics
# \B- avoids picks as F-14 as a negative number
\.{3,}| # ellipsis or sequences of dots
\w+| # alphanumerics
-+| # any sequence of dashes
\S # any non-space character
'''
RegexpTokenizer.__init__(self, tokenizer_regexp)
class tk(object):
def __init__(self):
self.tok = RegexpTokenizer(r'\b([a-zA-Z]+)\b')
self.stemmer = LancasterStemmer()
def __call__(self, doc):
return [self.stemmer.stem(s) for s in self.tok.tokenize(doc)]
# define the word list to be ignored
stop_words = text.ENGLISH_STOP_WORDS
def prep_string(s):
s = re.sub("\n", " ", s)
s = re.sub("\>", " ", s)
#toks = Token(TEXT=s, LOC=CharSpanLocation(0, len(s), 's'))
wordre = r"\w+@[\w.]+|'s|[0-9]+[.0-9]+|[0-9]+|^\w+:|([A-Z][.]+)+|(\w+[-']?)+|[.!?]|:\w*\n"
toks = RegexpTokenizer(wordre).tokenize(s)
word_list = []
for tok in toks:
word_list.append(tok)
return word_list
def getTokenCount(description):
tokens = RegexpTokenizer(r'\w+').tokenize(description)
tokens = [w.lower() for w in tokens]
stopwords = yaml.load(open("backend/nltk/stopwords.yaml", "r"))
tokens = [w for w in tokens if not w in stopwords]
tokens = [w for w in tokens if len(w) > 2]
stemmer = PorterStemmer()
tokens = [stemmer.stem(w) for w in tokens]
tokenCount = collections.Counter(tokens)
return tokenCount
def parsing(self, line):
line = line[:-1] #문장 맨 뒤 '\n' 자르기
# 매개로 들어온 line을 tab 단위로 잘라 tokens list에 저장
tokenizer = RegexpTokenizer("\t", gaps=True)
tokens = tokenizer.tokenize(line)
count = 1
for token in tokens :
if count == 1 :
self.instruction = token
elif count == 2 :
self.format = int(token)
elif count == 3 :
self.opcode = int(token, 16)
elif count == 4 :
self.numberOfOperand = int(token)
else :
print("[InstTable.py] parsing() error")
count += 1
class tokenizer(object):
def __init__(self):
self.tokenize=RegexpTokenizer(r'\b([A-Za-z]+)\b') #remove the punctuations
if ver==2:
self.stemmer = SnowballStemmer("english") #using stemmed version of words
elif ver==1:
self.stemmer = LancasterStemmer()
else:
self.stemmer = PorterStemmer()
def __call__(self, doc):
return [self.stemmer.stem(token) for token in self.tokenize.tokenize(doc)]
class Extracteur_Mots(BaseEstimator, TransformerMixin):
def __init__(self):
self.regexp = RegexpTokenizer("[a-z][a-z']{2,}")
def fit(self, comments, y=None):
return self
def transform(self, comments, y=None):
mots = []
for c in comments:
mots.append(self.regexp.tokenize(c.lower()))
return mots
def __init__(self, args, opts):
super(Method, self).__init__(args, opts)
self.es_int = ESInterface(host=self.opts.server,
port=self.opts.port,
index_name=self.opts.index_name)
self.regex_citation = re.compile(
r"\(\s?(([A-Za-z\-]+\s)+([A-Za-z\-]+\.?)?,?\s\d{2,4}(;\s)?)+\s?\)|"
r"(\[(\d+([,–]\s?)?)+\])|"
r"\[[\d,-]+\]").sub
self.all_digits = re.compile(r"^\d+$").search
if self.opts.stopwords_path:
stop_path = self.opts.stopwords_path
else:
stop_path = STOPWORDS_PATH
if self.opts.remove_stopwords:
with file(self.opts.stopwords_path) as f:
self.stopwords = frozenset([l.strip().lower() for l in f])
else:
self.stopwords = frozenset([])
self.tokenizer = RegexpTokenizer('[^\w\-\']+', gaps=True)
def parsing(self, line):
line = line[:-1] # 문장 맨 뒤 '\n' 자르기
# 매개로 들어온 line을 tab 단위로 잘라 tokens list에 저장
tokenizer = RegexpTokenizer("\t", gaps=True)
tokens = tokenizer.tokenize(line)
count = 0
for token in tokens:
count += 1
if count == 1:
self.label = token
elif count == 2:
self.operator = token
elif count == 3:
opnd = token
tokenizer = RegexpTokenizer(",", gaps=True)
opnds = tokenizer.tokenize(opnd)
i = 0
for op in opnds:
self.operand.append(op)
i += 1
elif count == 4:
self.comment = token
else:
print("[TokenTable.py] parsing() error")
def summarize(docid,score_dict,pos_dict):
global cnt
tokenizer = RegexpTokenizer('\w+') ##creating a tokenizer to match the words
snippet = ""
with open(path + "//" + docid,"r") as fi: ## open the extracted text file in read mode
file_text = fi.read()
tokens = tokenizer.tokenize(file_text) ## tokenize the text file using the tokenizer
if score_dict[docid] not in (0,-1): ## for normal phrase/word queries
cnt += 1
for pos in pos_dict[docid]: ## get snippets based on identified positions from the position dictionary
pos1 = abs(pos - 8) ## get the preceeding and following 8 words from the identified position in the text
pos2 = pos + 8
if pos1 < 0:
pos1 = 0
if pos2 > len(tokens):
pos2 = len(tokens)
snippet = ' '.join(tokens[pos1:pos2])
print docid,"\t",snippet ## display docid and snippet
elif score_dict[docid] == -1: ## to display document ids that do not contain the negated word/phrase
cnt += 1
print docid
def tokenizeText(self, text):
ret = RegexpTokenizer(u"(\d+\u00B0(\.)?)|(nr\.)|(\d+/\d+/eg)|(\d+\:\d+\w*)|(\d+\.\d+\w*)+|[\w\d]+|(\s\w\.)|(\.)|\,|\t|[^ \t\n\r\f\v\w\d]")
tokens = ret.tokenize(text)
ntokens = []
sentence = []
i = -1
for t in tokens[:-1] :
i += 1
if type(t) is StringType:
t = t.decode('UTF-8')
if (t.istitle() and tokens[i-1] == '.') or (regex.search(r'^\d+',t) and tokens[i+1].istitle()):
ntokens.append(sentence)
sentence = [t.lower().strip()]
else :
sentence.append(t.lower().strip())
sentence.append(tokens[-1].lower().strip())
ntokens.append(sentence)
return ntokens
def __init__(self, corpus, tokenize_str, delimiter, n, max_length):
self.corpus = corpus
self.tokenizer = RegexpTokenizer(tokenize_str)
self.delimiter = delimiter
self.n = n
self.max_length = max_length
# use set methods to set these variables
self.tokenized_corpus = []
self.startList = []
self.ngramDict = defaultdict(list)
self.unigramDict = defaultdict(list)
self.set_tokenized_corpus()
self.set_ngrams()
def __init__(self, args, opts):
super(Method, self).__init__(args, opts)
self.es_int = ESInterface(host=self.opts.server,
port=self.opts.port,
index_name=self.opts.index_name)
self.analyzer = self.es_int.get_index_analyzer()
self.regex_citation = re.compile(r"\(\s?(([A-Za-z\-]+\s)+([A-Za-z\-]+\.?)?,?\s\d{2,4}(;\s)?)+\s?\)|"
r"(\[(\d+([,–]\s?)?)+\])|"
r"\[[\d,-]+\]").sub
self.all_digits = re.compile(r"^\d+$").search
if self.opts.remove_stopwords:
with file(self.opts.stopwords_path) as f:
self.stopwords = frozenset([l.strip().lower() for l in f])
else:
self.stopwords = frozenset([])
self.db = MySQLdb.connect(host=constants.mysql_server,
port=constants.mysql_port,
user=constants.mysql_user,
passwd=constants.mysql_pass,
db=constants.mysql_db)
self.cur = self.db.cursor()
self.ttys = ['SY']
ttygroups = {"syns": ('AUN', 'EQ', 'SYN', 'MTH'),
"chemicals": ('CCN', 'CSN'),
"drugs": ('BD', 'BN', 'CD', 'DP', 'FBD', 'GN', 'OCD'),
"diseases": ('DI', ), "findings": ('FI', ),
"hierarchy": ('HS', 'HT', 'HX'), "related": ('RT', ),
"preferred": ('PTN', 'PT')}
self.doc_mod = documents_model.DocumentsModel(opts.anns_dir)
# self.ann_client = AnnotationsClient()
self.reg_apa = re.compile(
# [Chen et al.2000]
r"\(\s?(([A-Za-z\-]+\s)+([A-Za-z\-]+\.?)?,?\s\d{2,4}(;\s)?)+\s?\)|"
r"\(\s?([^ ]+\s?[^ ]*et\sal\.,?\s\d{2,4}(,\s)?)+(\sand\s)?[^ ]+\s?[^ ]*et\sal\.,?\s\d{2,4}\)|"
r"\w+\set al\. \(\d{2,4}\)") # [Chen et al. 200]
self.reg_apa_rare = re.compile(
r"((([A-Z]\w+\set\sal\.,? \d{4})|([A-Z]\w+\sand\s[A-Z]\w+,?\s\d{4}))((,\s)| and )?)+")
self.reg_apa2 = re.compile(
r"\(\s?(\w+\s?\w*et\sal\.,\s\d{2,4}(,\s)?)+(\sand\s)?\w+\s?\w*et\sal\.,\s\d{2,4}\)")
self.reg_ieee = re.compile(r"(\[(\d+([,–]\s?)?)+\])|\[\s?[\d,-]+\]")
self.reg_paranthesis = re.compile(
r"\(\s?\d{1,2}(,\s\d{1,2})*(\sand\s\d{1,2})?\)")
self.nlp_extractor = Extract_NLP_Tags()
self.tokenizer = RegexpTokenizer('[^\w\-\']+', gaps=True)
self.lmtzr = WordNetLemmatizer()
self.stemmer = stem.porter.PorterStemmer()
def __init__(self, args, opts):
super(Method, self).__init__(args, opts)
self.es_int = ESInterface(host=self.opts.server,
port=self.opts.port,
index_name=self.opts.index_name)
self.regex_citation = re.compile(r"\(\s?(([A-Za-z\-]+\s)+([A-Za-z\-]+\.?)?,?\s\d{2,4}(;\s)?)+\s?\)|"
r"(\[(\d+([,–]\s?)?)+\])|"
r"\[[\d,-]+\]").sub
self.all_digits = re.compile(r"^\d+$").search
if self.opts.stopwords_path:
stop_path = self.opts.stopwords_path
else:
stop_path = STOPWORDS_PATH
if self.opts.remove_stopwords:
with file(self.opts.stopwords_path) as f:
self.stopwords = frozenset([l.strip().lower() for l in f])
else:
self.stopwords = frozenset([])
self.tokenizer = RegexpTokenizer('[^\w\-\']+', gaps=True)
def tokenize(text, clean=True):
"""
Returns a list of lists of the tokens in text, separated by sentences.
Each line break in the text starts a new list.
:param clean: If True, performs some cleaning action on the text, such as replacing
numbers for the __NUMBER__ keyword (by calling :func:`clean_text`)
"""
ret = []
if type(text) != unicode:
text = unicode(text, 'utf-8')
if clean:
text = clean_text(text, correct=True)
else:
# replace numbers for __NUMBER__ and store them to replace them back
numbers = re.findall(ur'\d+(?: \d+)*(?:[\.,]\d+)*[²³]*', text)
numbers.extend(re.findall(ur'[²³]+', text))
text = re.sub(ur'\d+( \d+)*([\.,]\d+)*[²³]*', '__NUMBER__', text)
text = re.sub(ur'[²³]+', '__NUMBER__', text)
# clitic pronouns
regexp = r'''(?ux)
(?<=\w) # a letter before
-(me|
te|
o|a|no|na|lo|la|se|
lhe|lho|lha|lhos|lhas|
nos|
vos|
os|as|nos|nas|los|las| # unless if followed by more chars
lhes)(?![-\w]) # or digits or hyphens
'''
text = re.sub(regexp, r'- \1', text)
regexp = ur'''(?ux)
# the order of the patterns is important!!
([^\W\d_]\.)+| # one letter abbreviations, e.g. E.U.A.
__NUMBER__:__NUMBER__| # time and proportions
[DSds][Rr][Aa]?\.| # common abbreviations such as dr., sr., sra., dra.
[^\W\d_]{1,2}\$| # currency
\w+([-']\w+)*-?| # words with hyphens or apostrophes, e.g. não-verbal, McDonald's
# or a verb with clitic pronoun removed (trailing hyphen is kept)
-+| # any sequence of dashes
\.{3,}| # ellipsis or sequences of dots
__LINK__| # links found on wikipedia
\S # any non-space character
'''
# loads trained model for tokenizing Portuguese sentences (provided by NLTK)
sent_tokenizer = nltk.data.load('tokenizers/punkt/portuguese.pickle')
# the sentence tokenizer doesn't consider line breaks as sentence delimiters, so
# we split them manually.
sentences = []
lines = text.split('\n')
for line in lines:
sentences.extend(sent_tokenizer.tokenize(line, realign_boundaries=True))
t = RegexpTokenizer(regexp)
for p in sentences:
if p.strip() == '':
continue
# Wikipedia cleaning
if clean:
# discard sentences with troublesome templates or links
if any((x in p for x in ['__TEMPLATE__', '{{', '}}', '[[', ']]'])):
continue
new_sent = t.tokenize(p)
if clean:
# discard sentences that are a couple of words (it happens sometimes
# when extracting data from lists).
if len(new_sent) <= 2:
continue
elif len(numbers) > 0:
# put back numbers that were previously replaced
for i in xrange(len(new_sent)):
token = new_sent[i]
while '__NUMBER__' in token:
token = token.replace('__NUMBER__', numbers.pop(0), 1)
new_sent[i] = token
ret.append(new_sent)
return ret
return string.replace(u"\u2019", '')
def stringFromHTMLParagraph(paraWithTags):
paraString = ''
for taggedString in paraWithTags.strings:
paraString += removeApostrophe(taggedString.string)
return paraString
def titleFromArticleSoup(soup):
titleDiv = soup.find(class_ = 'story-heading')
if not titleDiv:
titleDiv = soup.find(class_ = 'entry-title')
return unicode(removeApostrophe(titleDiv.string))
# Set up the tokenizer and the tagger
tokenizer = RegexpTokenizer(r'\w+')
tagger = UnigramTagger(treebank.tagged_sents())
# Open up a redis connection
redisInterface = RedisInterface()
# Print status
print 'Reader ONLINE'
# Run the wait-execute loop
while True:
while not redisInterface.hasPending():
sleep(1)
page = redisInterface.popPending()
class Method(MethodInterface):
""" Produce reference text by submitting the
citance to the ElasticSearch server.
"""
method_opts = {'maxsize': {'type': int, 'default': 100},
'stopwords-path': {'default': STOPWORDS_PATH},
'remove-stopwords': {'default': False,
'action': 'store_true'},
'combine': {'default': False, 'action': 'store_true'},
'analyzer': {'default': False, 'type': str},
'ngram': {'default': False, 'type': int},
'concept_boost': {'default': 3, 'type': int},
'np_boost': {'default': 3, 'type': int},
'sent_boost': {'default': 1, 'type': int},
'stem_boost': {'default': 1, 'type': int},
'runmode': {'default': 'train'}}
def __init__(self, args, opts):
super(Method, self).__init__(args, opts)
self.es_int = ESInterface(host=self.opts.server,
port=self.opts.port,
index_name=self.opts.index_name)
self.analyzer = self.es_int.get_index_analyzer()
self.regex_citation = re.compile(r"\(\s?(([A-Za-z\-]+\s)+([A-Za-z\-]+\.?)?,?\s\d{2,4}(;\s)?)+\s?\)|"
r"(\[(\d+([,–]\s?)?)+\])|"
r"\[[\d,-]+\]").sub
self.all_digits = re.compile(r"^\d+$").search
if self.opts.remove_stopwords:
with file(self.opts.stopwords_path) as f:
self.stopwords = frozenset([l.strip().lower() for l in f])
else:
self.stopwords = frozenset([])
self.db = MySQLdb.connect(host=constants.mysql_server,
port=constants.mysql_port,
user=constants.mysql_user,
passwd=constants.mysql_pass,
db=constants.mysql_db)
self.cur = self.db.cursor()
self.ttys = ['SY']
ttygroups = {"syns": ('AUN', 'EQ', 'SYN', 'MTH'),
"chemicals": ('CCN', 'CSN'),
"drugs": ('BD', 'BN', 'CD', 'DP', 'FBD', 'GN', 'OCD'),
"diseases": ('DI', ), "findings": ('FI', ),
"hierarchy": ('HS', 'HT', 'HX'), "related": ('RT', ),
"preferred": ('PTN', 'PT')}
self.doc_mod = documents_model.DocumentsModel(opts.anns_dir)
# self.ann_client = AnnotationsClient()
self.reg_apa = re.compile(
# [Chen et al.2000]
r"\(\s?(([A-Za-z\-]+\s)+([A-Za-z\-]+\.?)?,?\s\d{2,4}(;\s)?)+\s?\)|"
r"\(\s?([^ ]+\s?[^ ]*et\sal\.,?\s\d{2,4}(,\s)?)+(\sand\s)?[^ ]+\s?[^ ]*et\sal\.,?\s\d{2,4}\)|"
r"\w+\set al\. \(\d{2,4}\)") # [Chen et al. 200]
self.reg_apa_rare = re.compile(
r"((([A-Z]\w+\set\sal\.,? \d{4})|([A-Z]\w+\sand\s[A-Z]\w+,?\s\d{4}))((,\s)| and )?)+")
self.reg_apa2 = re.compile(
r"\(\s?(\w+\s?\w*et\sal\.,\s\d{2,4}(,\s)?)+(\sand\s)?\w+\s?\w*et\sal\.,\s\d{2,4}\)")
self.reg_ieee = re.compile(r"(\[(\d+([,–]\s?)?)+\])|\[\s?[\d,-]+\]")
self.reg_paranthesis = re.compile(
r"\(\s?\d{1,2}(,\s\d{1,2})*(\sand\s\d{1,2})?\)")
self.nlp_extractor = Extract_NLP_Tags()
self.tokenizer = RegexpTokenizer('[^\w\-\']+', gaps=True)
self.lmtzr = WordNetLemmatizer()
self.stemmer = stem.porter.PorterStemmer()
# if len(args) > 3:s
# self.ttys = []
#
# for tty in args[3:]:
# if tty in ttygroups:
# self.ttys.extend(ttygroups[tty])
# else:
# self.ttys.append(tty)
def expand_concept(self, cdata, synonyms=False):
rejected_semTypes = {'ftcn', 'qlco', 'qnco', 'inpr'}
Okay = True
for st in cdata['SemanticTypes']:
if st in rejected_semTypes:
Okay = False
if Okay:
if synonyms:
return self.concept_synonyms(cdata['ConceptId'])
else:
return cdata['ConceptId']
def concept_synonyms(self, cui):
if cui in evaluate.cachefile:
return set(evaluate.cachefile[cui])
else:
termtypes = ("and (TTY=" +
" OR TTY=".join(["'%s'" % x for x in self.ttys]) + ")")
# query = 'select * from (select distinct STR from MRCONSO a,'+\
# '(select distinct CUI1,AUI1,AUI2,RELA,CUI2 from MRREL where cui1 = \'%s\'' % cui +\
# ' and rela is not null) b where a.CUI=b.CUI2 and a.LAT=\'ENG\') dd ;'
query = "select STR from MRCONSO where " +\
"CUI = '%s' and LAT = 'ENG' and ISPREF = 'Y'" % cui +\
termtypes + " and (SAB = 'SNOMEDCT_US')"
# print query
self.cur.execute(query)
# self.cur.execute("select STR from MRCONSO where " +
# "CUI = '%s' and LAT = 'ENG' and ISPREF = 'Y'" % cui +
# termtypes + " and SAB != 'CHV'")
syns = set(filter(lambda y: y.replace(" ", "").isalpha(),
[x.lower() for x, in self.cur.fetchall()]))
evaluate.cachefile[cui] = list(syns)
return syns
def run(self, test_data):
out_results = []
for ann in test_data:
doc_type = '_'.join((ann['topic_id'].lower(),
ann['reference_article'][:-4].lower()))
doc_type = doc_type.replace(',', '').replace("'", '"')
# TEMPORARY FIX FOR WRONG DOCUMENT TYPE NAME
if self.opts.runmode == 'eval':
doc_type = doc_type.replace('train', 'eval')
doc = self.doc_mod.get_doc(
ann['topic_id'].lower(), ann['citing_article'])
cit_text = ann['citation_text']
cit_text_doc = doc[
ann['citation_offset'][0]:ann['citation_offset'][1]]
cit_marker = ann['citation_marker']
cit_marker_doc = doc[
ann['citation_marker_offset'][0]:ann['citation_marker_offset'][1]]
cit_mrk_offset_sent = [ann['citation_marker_offset'][0] - ann['citation_offset'][0],
ann['citation_marker_offset'][1] - ann['citation_offset'][0]]
cleaned = self.reg_apa.sub('', cit_text_doc)
cleaned = self.reg_ieee.sub('', cleaned)
cleaned = self.reg_paranthesis.sub('', cleaned)
cleaned = self.reg_apa_rare.sub('', cleaned)
cleaned = re.sub('\s+', ' ', cleaned).strip()
cleaned = re.sub('(,\s)+', ', ', cleaned).strip(', ')
'''
-------------- IMMEDIATE NP BEFORE MARKER ----------
'''
m = list(self.reg_apa.finditer(cit_text_doc))
m1 = list(self.reg_ieee.finditer(cit_text_doc))
m2 = list(self.reg_paranthesis.finditer(cit_text_doc))
# (start, end, group)
if len(m) > 0:
markers = [(e.start(), e.end(), e.group(0)) for e in m]
elif len(m1) > 0:
markers = [(e.start(), e.end(), e.group(0))
for e in m1]
elif len(m2) > 0:
markers = [(e.start(), e.end(), e.group(0))
for e in m2]
else:
m3 = list(self.reg_apa_rare.finditer(cit_text_doc))
if len(m3) > 0:
markers = [(e.start(), e.end(), e.group(0))
for e in m3]
else:
markers = []
if len(markers) > 10000:
nps = self.nlp_extractor.parse_by_mbsp(cleaned.strip())
if nps is None:
q = cleaned
else:
t = nps.split(' ')
concepts = []
for i in range(len(t)):
conc = []
toks = t[i].split('/')
while(('NP' in toks[2]) and (i < len(t))):
conc.append((toks[0], toks[6]))
i += 1
if i < len(t):
toks = t[i].split('/')
if len(conc) > 0:
concepts.append(conc)
noun_phrases = [
' '.join([s1[0] for s1 in t1]) for t1 in concepts]
# nps = self.nlp_extractor.extract_NP(cleaned, mode='flattened')
# nps = [[[a[1:-1] for a in piece] for piece in sent] for sent in nps]
# nps = [a[1:-1] for sent in nps for piece in sent for a in piece]
# for e in nps:
# noun_phrases = [(sub_e[0].replace('"', ''),idx) for idx, sub_e in enumerate(e) if sub_e[0].replace('"', '') not in self.stopwords]
tokens = self.tokenizer.tokenize(cit_text)
tokens_offsets = self.tokenizer.span_tokenize(cit_text_doc)
nearest = ''
nearest_idx = -1
distance = 100000
# find nearest word to the citation marker
for idx, f in enumerate(tokens_offsets):
# check to see if in valid span (not citation markers)
invalid = False
for e in markers:
if f[0] >= e[0] and f[1] <= e[1]:
invalid = True
if (cit_mrk_offset_sent[0] - f[1] >= 0) and\
(cit_mrk_offset_sent[0] - f[1] < distance) and\
not invalid:
distance = cit_mrk_offset_sent[0] - f[1]
if len(re.findall(r"^[^A-Za-z]+$", tokens[idx])) == 0:
nearest = tokens[idx]
if (idx > 0) and len(re.findall(r"^[^A-Za-z]+$", tokens[idx - 1])) == 0:
nearest = tokens[
idx - 1] + ' ' + tokens[idx]
nearest_idx = idx
elif (cit_mrk_offset_sent[0] < f[1]):
break
if len(nearest.split(' ')) == 1 and nearest_idx > 0 and\
tokens[nearest_idx] not in stops100:
nearest = tokens[idx - 1] + ' ' + tokens[idx]
largest = 0
q = ''
for n in noun_phrases:
if (nearest in n) and (len(nearest.split()) > largest):
q = '"%s"' % nearest
largest = len(nearest.split())
if q == '':
q = cleaned
q = sanitize(q)
# find longest noun phrase containing the nearest
# res = None
# for np in nps[0]:
# if nearest in np and len(np) > longest and len(np) < 5:
# longest = len(np)
# res = np
# if res is not None:
# res = ' '.join([el for el in res])
# else:
# res = nearest
else:
try:
qtxt = unicodedata.normalize('NFKD',
cleaned).encode('ascii', 'ignore')
except:
qtxt = cleaned.encode('ascii', 'ignore')
qterms = [qtxt]
tokens = self.tokenizer.tokenize(' '.join(qterms))
# tokens = self.es_int.tokenize(qtxt, analyzer=self.analyzer)
q = ' '.join([t for t in tokens
if (t not in self.stopwords and
not(self.all_digits(t)))])
if self.opts.concept_boost > 0:
qconcepts = mmrun(cleaned)
qcids = []
for cdata in qconcepts['concepts']:
newterms = self.expand_concept(cdata)
if newterms is not None:
qcids.append(newterms)
else:
qcids = []
if self.opts.np_boost > 0:
nps = self.nlp_extractor.extract_NP(qtxt, mode='flattened')
noun_phs = set()
for e in nps:
for e1 in e:
if len(e1) < 4:
all_stop = False
if self.opts.remove_stopwords:
tmp = ' '.join(sub_e.replace('"', '')
for sub_e in e1 if sub_e.replace('"', '') not in self.stopwords)
else:
count = 0
for sub_e in e1:
if sub_e.replace('"', '') in self.stopwords:
count += 1
if count == len(e1):
all_stop = True
tmp = ' '.join(sub_e.replace('"', '')
for sub_e in e1)
if '"' + tmp.replace('"', '') + '"' not in noun_phs and not all_stop:
noun_phs.add(
'"' + tmp.replace('"', '') + '"')
else:
noun_phs = []
if self.opts.analyzer:
r = self.es_int.simple_search(q, maxsize=self.opts.maxsize,
source_fields=[
'offset', 'sentence'],
# field='sentence',
doc_type=doc_type,
params={'analyzer': self.opts.analyzer})
else:
# r = self.es_int.multi_field_search(sentence=q,
# concepts=' '.join(
# [w for w in qcids]),
# noun_phrases=' '.join(
# [e for e in noun_phs]),
# maxsize=self.opts.maxsize,
# source_fields=[
# 'offset', 'sentence', 'mm-concepts', 'noun_phrases'],
# doc_type=doc_type,
# field_boost=[self.opts.sent_boost,
# self.opts.concept_boost,
# self.opts.np_boost])
fields = [
'sentence', 'mm-concepts', 'noun_phrases_1', 'stemmed']
tokens1 = []
for w in self.tokenizer.tokenize(cleaned):
Okay = True
if self.opts.remove_stopwords:
if w in self.stopwords:
Okay = False
if '-' in w:
tokens1.append(self.stemmer.stem(w.replace('-', '')))
if Okay:
tokens1.append(self.stemmer.stem(w))
field_vals = [q, ' '.join([w for w in qcids]),
(' '.join([e for e in noun_phs])).replace(
'"', ''),
' '.join([w for w in tokens1])]
field_boosts = [
self.opts.sent_boost, self.opts.concept_boost, self.opts.np_boost, self.opts.stem_boost]
r = self.es_int.multi_field_search(field_vals=field_vals,
fields=fields,
source_fields=[
'offset', 'sentence'],
maxsize=self.opts.maxsize,
field_boost=field_boosts,
doc_type=doc_type)
# r = self.es_int.find_all(doc_type=doc_type, source_fields=['offset','sentence'])
for e in r:
fld = e.pop('fields')
e['offset'] = [eval(fld['offset'][0])]
# beg = e['offset'][0][0] - \
# 100 if e['offset'][0][0] else e['offset'][0][0]
# end = e['offset'][0][1] + 100
# e['offset'] = [(beg, end)]
e['sentence'] = fld['sentence'][0]
e['query'] = q
if self.opts.combine:
if len(r) == 0:
r = [{'_type': doc_type,
'_index': self.opts.index_name,
'_score': 0,
'score': 0,
'sentence': [''],
'offset': [(0, 1)],
'query':q, '_id':-11}]
r = [{'_type': r[0]['_type'],
'_index': r[0]['_index'],
'query': q,
'topic': ann['topic_id'].lower(),
'citance_number': ann['citance_number'],
'citation_text': ann['citation_text'],
'citing_article': ann['citing_article'],
'_score': sum([e['_score'] for e in r]),
'offset': [e['offset'][0] for e in r],
'sentence': [e['sentence'] for e in r],
'_id': '-000001'}]
out_results.append(r)
return out_results
class Tokenizer(object):
def __init__(self):
self.tok=RegexpTokenizer(r'\b([a-zA-Z]+)\b')
self.stemmer = LancasterStemmer()
def __call__(self, doc):
return [self.stemmer.stem(token) for token in self.tok.tokenize(doc)]
class NLPCorpus(object):
def __init__(self, corpus, tokenize_str, delimiter, n, max_length):
self.corpus = corpus
self.tokenizer = RegexpTokenizer(tokenize_str)
self.delimiter = delimiter
self.n = n
self.max_length = max_length
# use set methods to set these variables
self.tokenized_corpus = []
self.startList = []
self.ngramDict = defaultdict(list)
self.unigramDict = defaultdict(list)
self.set_tokenized_corpus()
self.set_ngrams()
def set_tokenized_corpus(self):
self.tokenized_corpus = [self.tokenizer.tokenize(sentence) for sentence in self.corpus.split(self.delimiter)]
# the last member is always empty, so remove it
self.tokenized_corpus.pop()
def set_ngrams(self):
for sentence in self.tokenized_corpus:
length = len(sentence)
#append empty string to indicate the end of a sentence
sentence.append('')
if(length >= self.n):
self.startList.append(tuple(sentence[0:self.n]))
for i in range(length):
self.unigramDict[sentence[i]].append(sentence[i+1])
if i <= (length - self.n):
self.ngramDict[tuple(sentence[i:i+self.n])].append(sentence[i+self.n])
else:
self.startList.append(tuple(sentence))
[self.unigramDict[sentence[j]].append(sentence[j+1]) for j in range(length)]
self.ngramDict[tuple(sentence)].append('')
def generate_sentence(self):
# the start of a generated sentence is always the start of a sentence from the corpus
key = choice(self.startList)
sentence = list(key)
sentence_length = len(" ".join(sentence))
# keep track of how many n-grams only have a single choice as the following word
single_choice = 0
while True:
if len(self.ngramDict[key]) == 1:
single_choice += 1
# use a unigram to select the next word to add more variety
if single_choice != 3:
# select one of the words mapped to the current ngram key
word = choice(self.ngramDict[key])
else:
word = choice(self.unigramDict[key[1]])
single_choice = 0
sentence_length += len(word) + 1
if sentence_length <= self.max_length and word:
sentence.append(word)
key = key[1:] + (word,)
else:
break
return " ".join(sentence)
def __init__(self):
"""Setup a new connection"""
print(yellow("Initializing new YakDB connection"))
self.db = YakDBDocumentDatabase()
# Initialize NLTK objects
self.nerTokenizer = RegexpTokenizer(r'\s+', gaps=True)
# lire le fichier "movies_coms.csv'
df = pd.read_csv('/home/paul/Downloads/movies_comments.csv')
# Afficher les deux premières lignes de df
df.head(10)
# Définition du dictionnaire :
sunText = ""
for words in df.Text:
sunText += " " + words
from sklearn.feature_extraction.text import CountVectorizer
from nltk.tokenize.regexp import RegexpTokenizer
tokenizer = RegexpTokenizer("[a-zA-Zé]{4,}")
vectorizer = CountVectorizer()
vectorizer.fit_transform(tokenizer.tokenize(sunText.lower()))
# Vectorization des mots :
def vect1(words):
liste = []
tokens = tokenizer.tokenize(words.lower())
for word in tokens:
liste.append(vectorizer.transform([word]).toarray())
return np.asarray(liste)
def convY(word_conv):
def tokenize(text):
str_ = "[A-Za-z]+"
regex_tokens = RegexpTokenizer(str_)
tokens = regex_tokens.tokenize(text.lower())
stems = stem_tokens(tokens, WNL)
return stems
def __init__(self):
RegexpTokenizer.__init__(self, r'[\w+#]+|[^\w\s]+')
def get_word_symbols_tokens(cls, text):
tokenizer = RegexpTokenizer('\s+', gaps=True)
return tokenizer.tokenize(text)
def get_word_tokens(cls, text):
tokenizer = RegexpTokenizer('\w+')
return tokenizer.tokenize(text)
def no_stop_tokens(self,text):
tokens = []
tokenizer = RegexpTokenizer('(\$?\d+\.\d+)|(([\w]+-)*[\w]+)')
tokens += tokenizer.tokenize(text)
return tokens
class Method(MethodInterface):
""" Produce reference text by submitting the
citance to the ElasticSearch server.
"""
method_opts = {'maxsize': {'type': int, 'default': 3},
'stopwords-path': {'default': STOPWORDS_PATH},
'remove-stopwords': {'default': False,
'action': 'store_true'},
'combine': {'default': False, 'action': 'store_true'},
'analyzer': {'default': False, 'type': str},
'ngram': {'default': False, 'type': int}}
def __init__(self, args, opts):
super(Method, self).__init__(args, opts)
self.es_int = ESInterface(host=self.opts.server,
port=self.opts.port,
index_name=self.opts.index_name)
self.regex_citation = re.compile(r"\(\s?(([A-Za-z\-]+\s)+([A-Za-z\-]+\.?)?,?\s\d{2,4}(;\s)?)+\s?\)|"
r"(\[(\d+([,–]\s?)?)+\])|"
r"\[[\d,-]+\]").sub
self.all_digits = re.compile(r"^\d+$").search
if self.opts.stopwords_path:
stop_path = self.opts.stopwords_path
else:
stop_path = STOPWORDS_PATH
if self.opts.remove_stopwords:
with file(self.opts.stopwords_path) as f:
self.stopwords = frozenset([l.strip().lower() for l in f])
else:
self.stopwords = frozenset([])
self.tokenizer = RegexpTokenizer('[^\w\-\']+', gaps=True)
def run(self, test_data):
# with codecs.open('tmp/test_data.json', 'wb', 'utf-8') as mf:
# json.dump(test_data, mf, indent=2)
out_results = []
det_res = {}
for ann in test_data:
doc_type = '_'.join((ann['topic_id'].lower(),
ann['reference_article'][:-4].lower()))
# TEMPORARY FIX FOR WRONG DOCUMENT TYPE NAME
doc_type = doc_type.replace('train', 'eval')
doc_type = doc_type.replace(',', '').replace("'", '"')
# TEMPORARY FIX FOR WRONG DOCUMENT TYPE NAME
doc_type = doc_type.replace('eval', 'train')
authors = set((ann['reference_article'][:-4].lower().strip(),
ann['citing_article'][:-4].lower().strip()))
# preprocess (removes citations) and tokenizes
# citation text before submitting to elasticsearch
q = self.regex_citation('', ann['citation_text'])
q = q.encode('ascii', 'ignore')
# tokens = self.es_int.tokenize(q, "sentence")
tokens = self.tokenizer.tokenize(q)
tokens = ['"' + t + '"' if '-' in t else t for t in tokens]
q = ' '.join([t for t in tokens
if (t not in self.stopwords and
t not in authors and
not(self.all_digits(t)))])
if self.opts.ngram:
tokens = self.es_int.tokenize(q, "sentence")
new_query = ''
for i in range(len(tokens) - self.opts.ngram):
tmp = ''
for j in range(i, i + self.opts.ngram):
tmp += tokens[j] + ' '
new_query += '"' + tmp.strip() + '" '
q = new_query.strip()
# q = '*:*'
if self.opts.analyzer:
r = self.es_int.simple_search(q, maxsize=self.opts.maxsize,
source_fields=[
'offset', 'sentence'],
# field='sentence',
doc_type=doc_type,
params={'analyzer': self.opts.analyzer})
else:
r = self.es_int.simple_search(q, maxsize=self.opts.maxsize,
source_fields=[
'offset', 'sentence'],
# field='sentence',
doc_type=doc_type)
for e in r:
fld = e.pop('fields')
e['offset'] = [eval(fld['offset'][0])]
# beg = e['offset'][0][0] - \
# 100 if e['offset'][0][0] else e['offset'][0][0]
# end = e['offset'][0][1] + 100
# e['offset'] = [(beg, end)]
e['sentence'] = fld['sentence'][0]
e['query'] = q
e['topic'] = ann['topic_id'].lower()
if self.opts.combine:
if len(r) == 0:
r = [{'_type': doc_type,
'_index': self.opts.index_name,
'_score': 0,
'sentence': '',
'offset': [(0, 1)],
'query':q, '_id':-11}]
r = [{'_type': r[0]['_type'],
'_index': r[0]['_index'],
'query': q,
'topic': ann['topic_id'].lower(),
'citance_number': ann['citance_number'],
'citation_text': ann['citation_text'],
'citing_article': ann['citing_article'],
'_score': sum([e['_score'] for e in r]),
'offset': [e['offset'][0] for e in r],
'sentence': [e['sentence'] for e in r],
'_id': '-000001'}]
out_results.append(r)
# with codecs.open('tmp/out_results.json', 'wb', 'utf-8') as mf:
# json.dump(out_results, mf, indent=2)
# sys.exit()
return out_results
def words(self,text):
reg_words = r'[\#|\$|\w|َ|ِ|ً|ٌ|ٍ|ْ|ّ|ُ]+'
tokenizer = RegexpTokenizer(reg_words, flags=re.UNICODE|re.IGNORECASE)
return tokenizer.tokenize(text)
def __init__(self):
RegexpTokenizer.__init__(self, r'\w+[-\w+]*|[^\w\s]+')
def __init__(self):
self.tok=RegexpTokenizer(r'\b([a-zA-Z]+)\b')
self.stemmer = LancasterStemmer()
class TranslatronProtocol(WebSocketServerProtocol):
def __init__(self):
"""Setup a new connection"""
print(yellow("Initializing new YakDB connection"))
self.db = YakDBDocumentDatabase()
# Initialize NLTK objects
self.nerTokenizer = RegexpTokenizer(r'\s+', gaps=True)
def onConnect(self, request):
pass
def onOpen(self):
pass
def performDocumentSearch(self, query):
"""
Perform a token search on the document database.
Search is performed in multi-token prefix (all must hit) mode.
Tokens with no hits at all are ignored entirely
"""
startTime = time.time()
queryTokens = map(str.lower, word_tokenize(query))
levels = [b"title", b"content", b"metadata"]
#Remove 1-token parts from the query -- they are way too general!
#Also remove exclusively-non-alnum tokens
queryTokens = [tk for tk in queryTokens if (len(tk) > 1 and has_alpha_chars(tk))]
results = self.db.searchDocumentsMultiTokenPrefix(queryTokens, levels=levels)
#Return only those paragraphs around the hit paragraph (or the first 3 pararaphs)
for hitLocation, doc in results.items():
(docId, docLoc) = InvertedIndex.splitEntityIdPart(hitLocation)
#Compute which paragraphs to display
minShowPar = 0
maxShowPar = 2
if docLoc.startswith(b"paragraph"):
paragraphNo = int(docLoc[9:])
minShowPar = max(0, paragraphNo - 1)
maxShowPar = min(len(doc[b"paragraphs"]), paragraphNo + 1)
#Modify documents
results[hitLocation][b"hitLocation"] = docLoc
results[hitLocation][b"paragraphs"] = doc[b"paragraphs"][minShowPar:maxShowPar]
# Measure timing
timeDiff = (time.time() - startTime) * 1000.0
print("Document search for %d tokens took %.1f milliseconds" % (len(queryTokens), timeDiff))
return results
def uniquifyEntities(self, entities):
"""Remove duplicates from a list of entities (key: ["id"])"""
seen = set()
result = []
for entity in entities:
itemId = entity[b"id"]
if itemId in seen: continue
seen.add(itemId)
result.append(entity)
return result
def performEntitySearch(self, query):
"""
Search entities. Tokens are not splitted in order to allow simple search
for multi-token entities like "Biological process"
"""
results = self.db.searchEntitiesSingleTokenMultiExact([query], level=b"aliases")
#Return only result array. TODO can't we just use results[query]
if query not in results:
return []
return results[query]
def filterNERTokens(self, token):
"""
Filter function to remove stuff that just clutters the display.
"""
#Short numbers are NOT considered database IDs.
#NOTE: In reality, pretty much all numbers are Allergome database IDs, e.g. see
# http://www.allergome.org/script/dettaglio.php?id_molecule=14
if len(token) <= 5 and token.isdigit():
return False
return True
def performEntityNER(self, query):
"Search a query text for entity/entity alias hits"
startTime = time.time()
tokens = self.nerTokenizer.tokenize(query)
queryTokens = [s.encode("utf-8") for s in tokens]
# Search for case-sensitive hits
searchFN = InvertedIndex.searchSingleTokenMultiExact
results = searchFN(self.db.entityIdx.index, frozenset(filter(self.filterNERTokens, queryTokens)), level=b"aliases")
# Results contains a list of tuples (dbid, db) for each hit. dbid is db + b":" + actual ID
# We only need the actual ID, so remove the DBID prefix (which is required to avoid inadvertedly merging entries).
# This implies that the DBID MUST contain a colon!
results = {k: [(a.partition(b":")[2], b) for (a, b) in v] for k, v in results.items() if v}
#
# Multi-token NER
# Based on case-insensitive entries where only the first token is indexed.
#
# TESTING: Multi token NER
lowercaseQueryTokens = [t.lower() for t in queryTokens]
t1 = time.time()
ciResults = searchFN(self.db.entityIdx.index, frozenset(lowercaseQueryTokens), level=b"cialiases")
t2 = time.time()
print("TX " + str(t2 - t1))
for (firstTokenHit, hits) in ciResults.items():
#Find all possible locations where the full hit could start, i.e. where the first token produced a hit
possibleHitStartIndices = [i for i, x in enumerate(lowercaseQueryTokens) if x == firstTokenHit]
#Iterate over all possible
for hit in hits:
hitLoc, _, hitStr = hit[1].rpartition(b"\x1D") # Full (whitespace separated) entity name
if not hitStr: continue #Ignore malformed entries. Should usually not happen
hitTokens = [t.lower() for t in hitStr.split()]
numTokens = len(hitTokens)
#Check if at any possible hit start index the same tokens occur (in the same order )
for startIdx in possibleHitStartIndices:
actualTokens = lowercaseQueryTokens[startIdx : startIdx+numTokens]
#Check if the lists are equal. Shortcut for single-token hits
if numTokens == 1 or all((a == b for a, b in zip(actualTokens, hitTokens))):
#Reconstruct original (case-sensitive) version of the hit
csTokens = queryTokens[startIdx : startIdx+numTokens]
#NOTE: This MIGHT cause nothing to be highlighted, if the reconstruction
# of the original text is not equal to the actual text. This is true exactly
# if the tokenizer removes or changes characters besides whitespace in the text.
csHit = b" ".join(csTokens)
# Emulate defaultdict behaviour
if not csHit in results: results[csHit] = []
results[csHit].append((hitStr, hitLoc))
t3 = time.time()
print("TY " + str(t3 - t2))
# TODO: Remove results which are subsets of other hits. This occurs only if we have multi-token results
removeKeys = set() # Can't modify dict while iterating it, so aggregate keys to delete
for key in results.keys():
# Ignore single part results
if any((chr(c).isspace() for c in key)):
tokens = key.split()
for token in tokens:
# Remove sub-hit in results.
# This avoids the possibility of highlighting the smaller hit
if token in results:
removeKeys.add(token)
# Remove aggregated keys
for key in removeKeys:
del results[key]
# Result: For each token with hits --> (DBID, Database name)
# Just takes the first DBID.It is unlikely that different DBIDs are found, but we
# can only link to one using the highlighted label
ret = {k: (v[0][0], v[0][1]) for k, v in results.items() if v}
# Measure timing
timeDiff = (time.time() - startTime) * 1000.0
print("NER for %d tokens took %.1f milliseconds" % (len(queryTokens), timeDiff))
return ret
def onMessage(self, payload, isBinary):
request = json.loads(payload.decode('utf8'))
# Perform action depending on query type
qtype = request["qtype"]
if qtype == "docsearch":
results = self.performDocumentSearch(request["term"])
del request["term"]
request["results"] = list(results.values())
elif qtype == "ner":
results = self.performEntityNER(request["query"])
del request["query"]
request["results"] = results
elif qtype == "metadb":
# Send meta-database to generate
request["results"] = metaDB
elif qtype == "entitysearch":
request["entities"] = self.performEntitySearch(request["term"])
del request["term"]
elif qtype == "getdocuments":
# Serve one or multiple documents by IDs
docIds = [s.encode() for s in request["query"]]
request["results"] = self.db.docIdx.findEntities(docIds)
del request["query"]
else:
print(red("Unknown websocket request type: %s" % request["qtype"], bold=True))
return # Do not send reply
#Return modified request object: Keeps custom K/V pairs but do not re-send query
self.sendMessage(json.dumps(request, default=documentSerializer).encode("utf-8"), False)
def onClose(self, wasClean, code, reason):
print("WebSocket connection closed: {0}".format(reason))