def extract_names(sentences, email, github_username):
names = []
email_name = email.split('@')
email_name = email_name[0]
for tagged_sentence in sentences:
for chunk in tagged_sentence:
try:
if chunk[1] == "NNP":
names.append(chunk[0])
except:
pass
name_dict = None
git_name_dict = None
for name in names:
ratio = fuzz.ratio(email_name.lower(), name.lower())
if name_dict:
largest_ratio = name_dict[1]
if ratio > largest_ratio:
name_dict = [name, ratio]
else:
name_dict = [name, ratio]
if github_username:
git_ratio = fuzz.ratio(github_username.lower(), name.lower())
if git_name_dict:
git_largest_ratio = git_name_dict[1]
if git_ratio > git_largest_ratio:
git_name_dict = [name, git_ratio]
else:
git_name_dict = [name, git_ratio]
if git_name_dict:
if git_name_dict[1] > name_dict[1]:
return git_name_dict[0]
return name_dict[0]
def extract_github_url(sentences):
base_url = "https://github.com/"
names = []
urls = []
for tagged_sentence in sentences:
for chunk in tagged_sentence:
try:
if "github.com" in chunk[0]:
urls.append(chunk[0])
if chunk[1] == "NNP":
names.append(chunk[0])
except:
pass
name_dict = None
has_found = False
is_valid = False
for url in urls:
url_segments = url.split('/')
username = url_segments[-1]
github_url = base_url + username
req = requests.get(github_url)
if req.status_code == requests.codes.ok:
return username, github_url
for name in names:
if has_found:
username = name
github_url = base_url + username
req = requests.get(github_url)
if req.status_code == requests.codes.ok:
return username, github_url
else:
if "github" in name.lower():
has_found = True
def extractnames(self, sentences):
names = []
for tagged_sentence in sentences:
for chunk in nltk.ne_chunk(tagged_sentence):
if type(chunk) == nltk.tree.Tree:
if chunk.label() == 'PERSON':
names.append(' '.join([c[0] for c in chunk]))
return names
def getNames(self, dataFrame):
names = []
for index, splitchunk in dataFrame.iterrows():
if not self.isValidName(splitchunk["name"]):
continue
names.append(splitchunk["name"])
return names
def get_names(text):
doc = nlp(text)
names = []
for x in doc.ents:
if x.label_ == 'PERSON':
names.append(x.text)
names = list(set(names))
return names
def get_names(self, list_) -> list:
names = []
utils = Utils()
for splitchunk in list_:
if not utils.isValidName(splitchunk.name):
continue
names.append(splitchunk.name)
return names
def get_names(data): names=[] for sent in nltk.sent_tokenize(doc): for chunk in nltk.ne_chunk(nltk.pos_tag(nltk.word_tokenize(sent))): if hasattr(chunk, 'node'): if chunk.node=='PERSON': leaf=chunk.leaves()[0] name = ' '.join(c[0] for c in chunk.leaves()) names.append(name) return names
def get_names(self, gender):
if gender == "m":
path = "male_names.txt"
alternate_path = "tools/male_names.txt"
else:
path = "female_names.txt"
alternate_path = "tools/female_names.txt"
names = []
try:
with open(path, "r") as f:
for name in f:
names.append(name.strip().title())
except FileNotFoundError:
with open(alternate_path, "r") as f:
for name in f:
names.append(name.strip().title())
return names
def find_names(text, tags):
"""
Finds names with a Stanford tagger. These are assumed to be a speaker name if no speaker has been found.
:param text: The text to search.
:param tags: A list of accumulated strings which have been tagged so far.
:return: The updated list of accumulated tags.
"""
stanford_tagger = StanfordNERTagger(STANFORD_TAGGER_DICTIONARY,
STANFORD_TAGGER_PATH,
encoding='utf-8')
tokenised = nltk.word_tokenize(text)
classified = stanford_tagger.tag(tokenised)
names = []
current_name = ""
found_name = False
for token in classified:
'''
The tag is in the following format:
(string, tag)
'''
token_word = token[0]
token_tag = token[1]
is_shortened_name = re.match("^\w+\.$", token_word)
if token_tag == "PERSON" or token_word in NAMES or token_word.lower(
) in TITLES or is_shortened_name is not None or token_word in string.punctuation:
if found_name is True and token_word not in string.punctuation:
current_name += " " + token_word
else:
current_name += token_word
found_name = True
elif token_tag != "PERSON" and found_name:
names.append(current_name)
current_name = ""
found_name = False
lines = text.splitlines()
for line in lines:
stripped_line = line.strip()
if stripped_line in names:
tags.add((line, SPEAKER_TAG))
break
return tags
nouns = []
for la in tagged:
if (la[1].lower() == 'nnp'):
nouns.append(la[0].lower())
name = raw_input("Enter name of freedom fighter")
if name in mynames:
print "Associated: 1.Names 2.Locations"
ch = raw_input("Type 1 or 2")
ch = int(ch)
if ch == 1:
names = []
for noun in nouns:
for name1 in mynames:
if (noun == name):
names.append(noun)
names = list(set(names))
names = names.remove(name)
print "The names of freedom fighters associated with " + name + " are:"
for i in names:
print i
elif ch == 2:
locations = []
for noun in nouns:
for loc in mylocations:
if (noun == loc):
locations.append(noun)
locations = list(set(locations))
print "The location associated with " + name + " are:"
nouns=[]
for la in tagged:
if (la[1].lower() == 'nnp'):
nouns.append(la[0].lower())
name=raw_input("Enter name of freedom fighter")
if name in mynames:
print "Associated: 1.Names 2.Locations"
ch=raw_input("Type 1 or 2")
ch=int(ch)
if ch==1:
names=[]
for noun in nouns:
for name1 in mynames:
if (noun == name):
names.append(noun)
names=list(set(names))
names=names.remove(name)
print "The names of freedom fighters associated with "+name+" are:"
for i in names:
print i
elif ch==2:
locations=[]
for noun in nouns:
for loc in mylocations:
if (noun == loc):
locations.append(noun)
locations=list(set(locations))
print "The location associated with "+name+" are:"