def myTokenizer(self, txt):
"""
Constructing a tokenizer based on regular expressions
@params txt: the words of the text as a string
@rtype: {List}
"""
return re.findall(r"\w+(?:[-']\w+)*|'|[-.(]+|\S\w*", txt)
def findCurrency(text):
""" Display information about the found strings """
symbols = "$£eurospoundsdollars"
# Iterate through each item in text and find all strings matching a regular
# expression to find all amounts of money
for i in text:
matches = re.findall('((?:(?:\$|£)(?:\d+)(?:\.?\d*,?\d{1,3})(?:bn|m)?)|'\
'(?:(?:\d+)(?:\.?,?\d)*(?:bn|m)?(?: ?euros?| ?dollars?| ?pounds?| ?p)))',\
i, re.IGNORECASE)
# If a match is found, check the currency and amount, print
if matches:
for m in matches:
if re.search('\$|dollars?', m, re.IGNORECASE):
currency = "Dollar"
if re.search('\£|pounds?|p', m, re.IGNORECASE):
currency = "Pound"
if re.search('euros?', m, re.IGNORECASE): currency = "Euro"
amount = m.strip(symbols)
print("Found a match!" + "\nCurrency:", currency, "\nAmount:",\
amount, "\n")
def sentence2word(inputFile,outputFile):
with open(inputFile) as dataFile:
sentences = dataFile.read().splitlines()
rows = []
for sentence in sentences:
row = []
row.append(sentence); row.append('Sentence'); rows.append(row)
row = []
row.append('BOS'); row.append('BOS'); rows.append(row)
# split sentence into words and punctuations
words = re.findall(r"[\w']+|[().,!?;]", sentence)
for word in words:
row = []
row.append(word)
row.append('O')
rows.append(row)
row = []
row.append('EOS'); row.append('EOS'); rows.append(row)
with open(outputFile,'w') as w:
writer = csv.writer(w)
writer.writerows(rows)
print('Done: sentence text to word+\'O\' csv')
def convert_emphesize(text, return_count=False):
emphs = re.findall(r'\b[A-Z]{2,}\b', text)
emphs = set(emphs)
if return_count:
return len(emphs)
for emph_ in emphs:
text = re.sub(r'\b' + emph_ + r'\b', emph_ + ' emphh', text)
return text
def strip_url(text, return_count=False):
urls = re.findall('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', text)
if return_count:
return len(urls)
for url in urls:
text = text.replace(url, '_URL_')
text = text.replace('https:', '')
return text
def is_long_number(text, threshold=1, flag_res=False):
numbers_lens = re.findall('\\d+', text)
if numbers_lens and len(max(numbers_lens, key=len)) >= threshold:
if flag_res:
return len(max(numbers_lens, key=len))
return text + ' _longnumber_'
if flag_res:
return 0
return text
def tokenize(str):
# remove punctuation
tokens = re.findall(r"<a.*?/a>|<[^\>]*>|[\w'@#]+", str.lower())
# lemmatize words. try both noun and verb lemmatizations
lmtzr = WordNetLemmatizer()
for i in range(0, len(tokens)):
res = lmtzr.lemmatize(tokens[i])
if res == tokens[i]:
tokens[i] = lmtzr.lemmatize(tokens[i], 'v')
else:
tokens[i] = res
return tokens
def get_score(self, text):
if self.language == 'es':
sentences = len(self.sentence_tokenizer.tokenize(text))
clean_text = self.clean_text(text)
words = len(clean_text.split())
syllables = len(re.findall('[aeiou]', clean_text))
score = 206.835 - (62.3 *
(syllables / words)) - (words / sentences)
return score
elif self.language == 'en':
total_words = len(text.split())
characters = len("".join(self.clean_text(text).split()))
sentences = len(self.sentence_tokenizer.tokenize(text))
score = round((4.71 * (characters / total_words)) +
(0.5 * (total_words / sentences)) - 21.43)
return score
def stemming(string, top):
string_rep = re.sub('[^a-zA-Z]', ' ', string)
string_rep = string_rep.lower()
new_vocab = word_tokenize(string_rep)
#inter_stem=[ps.stem(l) for l in new_vocab]
vocab_stem = [
ps.stem(w) for w in new_vocab
if w not in set(stopwords.words('english'))
]
dictry_stem = set(vocab_stem)
data = ' '.join(vocab_stem)
arr = []
c = 0
t = 0
for i in dictry_stem:
c = len(re.findall(i, data))
t = t + c
arr.append([i, c])
#print(i,c)
aux = []
for q, w in arr:
if w > 1:
aux.append([q, w])
aux.sort(key=lambda i: i[1], reverse=True)
aux = [w for w in aux if w[1] > 1]
x = []
y = []
for wrd, co in aux:
x.append(wrd)
y.append(co)
print("total number of words:", t, "\t set of words:",
len(set(dictry_stem)))
#print("Top ",top," words occurring more than twice: ",aux)
print("%age of total length these word account for:", (sum(y) / t) * 100)
#plt.bar(x,y,color='red',alpha=0.8)
#plt.xlabel('Word')
#plt.ylabel('Frequency')
#plt.title('Words occurring more than twice')
return (x, string_rep)
s2 = '</s>'
Know = vocabRaw_tokens_nopunct[0] + vocabRaw_tokens_nopunct[
1] + vocabRaw_tokens_nopunct[2]
vocabRaw_tokens_nopunct.append("[^" + Know + "]")
vocabRaw_tokens_nopunct.append(s1)
Px_a = [0.0, 0.0, 0.0, 0.0, 0.0]
Px_b = [0.0, 0.0, 0.0, 0.0, 0.0]
Px_c = [0.0, 0.0, 0.0, 0.0, 0.0]
Px_UNK = [0.0, 0.0, 0.0, 0.0, 0.0]
Px_s = [0.0, 0.0, 0.0, 0.0, 0.0]
Ps_x = [0.0, 0.0, 0.0, 0.0, 0.0]
for i in range(0, 5):
Px_a[i] = len(
re.findall(
s1 + ".*" + vocabRaw_tokens_nopunct[0] + " " +
vocabRaw_tokens_nopunct[i] + ".*" + s2, dataRaw)) / len(
re.findall(vocabRaw_tokens_nopunct[0], dataRaw))
Px_b[i] = len(
re.findall(
s1 + ".*" + vocabRaw_tokens_nopunct[1] + " " +
vocabRaw_tokens_nopunct[i] + ".*" + s2, dataRaw)) / len(
re.findall(vocabRaw_tokens_nopunct[1], dataRaw))
Px_c[i] = len(
re.findall(
s1 + ".*" + vocabRaw_tokens_nopunct[2] + " " +
vocabRaw_tokens_nopunct[i] + ".*" + s2, dataRaw)) / len(
re.findall(vocabRaw_tokens_nopunct[2], dataRaw))
Px_UNK[i] = len(
re.findall(
s1 + ".*" + vocabRaw_tokens_nopunct[3] + " " +
def horus_to_features(horusfile, le):
print horusfile
features, sentence_shape = [], []
targets, tokens_shape, y_sentences_shape, y_tokens_shape = [], [], [], []
df = pd.read_csv(horusfile,
delimiter=",",
skiprows=1,
header=None,
keep_default_na=False,
na_values=['_|_'])
oldsentid = df.get_values()[0][1]
for index, linha in df.iterrows():
if len(linha) > 0:
if linha[7] == 0: #no compounds
if linha[1] != oldsentid:
sentence_shape.append(features)
y_sentences_shape.append(targets)
targets, features = [], []
idsent = linha[1]
idtoken = linha[2]
pos_bef = ''
pos_aft = ''
if index > 0 and df.get_value(index - 1, 7) == 0:
pos_bef = df.get_value(index - 1, 5)
if index + 1 < len(df) and df.get_value(index + 1, 7) == 0:
pos_aft = df.get_value(index + 1, 5)
token = linha[3]
postag = linha[5]
one_char_token = len(token) == 1
special_char = len(
re.findall('(http://\S+|\S*[^\w\s]\S*)', token)) > 0
first_capitalized = token[0].isupper()
capitalized = token.isupper()
title = token.istitle()
digit = token.isdigit()
stop_words = token in stop
small = True if len(horusfile[3]) <= 2 else False
stemmer_lanc = lancaster_stemmer.stem(token)
nr_images_returned = linha[17]
nr_websites_returned = linha[25]
hyphen = '-' in token
cv_loc = float(linha[12])
cv_org = float(linha[13])
cv_per = float(linha[14])
cv_dist = float(linha[15])
cv_plc = float(linha[16])
tx_loc = float(linha[20])
tx_org = float(linha[21])
tx_per = float(linha[22])
tx_err = float(linha[23])
tx_dist = float(linha[24])
if linha[6] in definitions.NER_TAGS_LOC: ner = u'LOC'
elif linha[6] in definitions.NER_TAGS_ORG: ner = u'ORG'
elif linha[6] in definitions.NER_TAGS_PER: ner = u'PER'
else: ner = u'O'
#standard shape
sel_features = [
idsent, idtoken, token,
token.lower(), stemmer_lanc, pos_bef, postag, pos_aft,
definitions.KLASSES2[ner],
le.transform(pos_bef),
le.transform(postag),
le.transform(pos_aft), title, digit, one_char_token,
special_char, first_capitalized, hyphen, capitalized,
stop_words, small, nr_images_returned,
nr_websites_returned, cv_org, cv_loc, cv_per, cv_dist,
cv_plc, tx_org, tx_loc, tx_per, tx_dist, tx_err
]
features.append(sel_features)
if linha[51] in definitions.NER_TAGS_LOC: y = u'LOC'
elif linha[51] in definitions.NER_TAGS_ORG: y = u'ORG'
elif linha[51] in definitions.NER_TAGS_PER: y = u'PER'
else: y = u'O'
targets.append(y)
tokens_shape.append(sel_features[9:len(sel_features)])
y_tokens_shape.append(definitions.KLASSES2[y])
oldsentid = linha[1]
print 'total of sentences', len(sentence_shape)
print 'total of tokens', len(tokens_shape)
return sentence_shape, y_sentences_shape, tokens_shape, y_tokens_shape
def extract_named_entity(named_entities_tree, entity_type):
return map(
lambda result: ' '.join(
map(lambda inner_result: inner_result.split('/')[0], result[5:]
[:-1].split())),
re.findall('\({}.*\)'.format(entity_type), named_entities_tree))
# 100 and returned to string format. After the validation the output is provided saying a match is found,
# what the match is, the currency type and the quanity.
#-----------------------------------------------------------------------------------------------------------
import urllib
import nltk
from nltk import re
# Prints presentation text.
print("=" * 80 + "\n PART 2 : REGULAR EXPRESSIONS, FSAs and FSTs \n" + "=" * 80)
# Add the website source and scrape the contents of the <p> tags.
print("Loading website 'http://www.bbc.co.uk/news/business-41779341'...")
website = urllib.urlopen('http://www.bbc.co.uk/news/business-41779341').read()
print("Finding text between <p></p> tags...")
pTagText = re.findall('<p>(.*?)</p>',website, flags=re.I)
# The regular expression.
regular_expression = '(?:([€$£])((?:\d\d{0,2})(?:,\d{3})*(?:\.\d+)?(?:k|mn|bn|tn)?))|(?:((?:\d\d{0,2})(?:,\d{3})*(?:\.\d+)?(?:k|mn|bn|tn)?)( ?pence|p| ?euros?| ?dollars?| ?pounds?))'
# Iterates through the text gained from the <p> tags and applies the regular expression on them to search
# for matches, which if found, are added to the results array.
results = []
print("Finding currency related text from website contents...\n")
for p in pTagText:
results += re.findall(regular_expression, p, flags=re.I)
# If no results are found print so.
if(len(results) == 0):
print("No matches found.")
import nltk
from nltk import word_tokenize, re, pos_tag
import nltk
from nltk import re
#We ask User to input whether he wants to input text or URL.
choose = input(
"Please let us know, if you want to search phone numbers from text or URL?"
)
if choose == 'text':
#If choice =text we input text from user, and store the Regular Expression pattern in reg
text = input("Please Enter a text:")
reg = '(((^)|(\s))((\+\d{2}\s\d{2}\s{0,1}\d{8})|([1-9]\d{3}\s\d{6})|((0)((0\d{2}\s\d{2}\s{0,1}\d{8})|([1-9]\d{3}\s{0,1}\d{6})|(\d{2}\s\d{2}\s{0,1}\d{8})))))'
phone = re.findall(reg, text)
print(text)
instate = 'q0'
if not phone:
print("Sorry No MatchesFound")
else:
for i in range(len(phone)):
print("Match Found:", phone[i][0], "at:", i)
#Getting PhoneNumbers from URL:
#If user wishes to enter URL, we ask user to enter URL
elif choose == "URL":
url = input("Please Enter the URL in full standard format:")
#We use urlopen to open that URL, and we decode and store the URL value in rw in html format
response = request.urlopen(url)
def tokens(text):
return re.findall('[a-z]+', text.lower())
f=open("SimLex999-100.txt","r")
lines=f.readlines()
#lines=str(lines)
result=[]
from nltk import re
lis1=[]
for i in range(0,len(lines)) :
lis1=lis1+lines[i].split('\t')
lis1
strtry=str(lis1)
strtry=" ".join(re.findall("[a-zA-Z]{2,}", strtry))
#print(strtry)
las=strtry.split(' ')
las
sent1 = list()
sent2 = list()
index = 0
for letter in las:
if index % 2 != 0:
sent2.append(letter)
else:
sent1.append(letter)
index += 1
from nltk.corpus import wordnet
syn_name1 = list()
for i in range(0,len(sent1)):
syn = wordnet.synsets(sent1[i])[0]
syn_name1.append(syn.name())
syn_name1
from nltk.corpus import wordnet
syn_name2 = list()