julian.kunkel
/
thesis-anne
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

updated labeling

This commit is contained in:
Anne Lorenz 2018-12-21 13:50:58 +01:00
parent ee911377bf
commit 9367457199
5 changed files with 534 additions and 986 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

580
data/interactive_labeling.csv
View File

File diff suppressed because one or more lines are too long

BIN
obj/dict_articles_organizations_without_banks.pkl Normal file
View File

Binary file not shown.

874
src/2018-12-01-al-interactive-labeling.ipynb
View File

File diff suppressed because it is too large Load Diff

4
src/MNBInteractive.py
View File

@ -94,7 +94,9 @@ class MNBInteractive:
# classes in order used
classes = classifier.classes_
class_count = classifier.class_count_
print('# MNB: ending multinomial naive bayes')
# return classes and vector of class estimates
return classes, class_probs
return classes, class_count, class_probs

62
src/NER.py
View File

@ -193,23 +193,51 @@ class NER:
n_dict[next_highest[0]] = next_highest[1]
print(n_dict)
def remove_banks_from_dict():
''' removes bank, news agencies and other organizations we do not need
'''
# load pickle object
with open('../obj/dict_articles_organizations.pkl', 'rb') as input:
dict = pickle.load(input)
black_list = ['Eastern and Southern African Trade and Development Bank', 'PTA Bank', 'Citigroup',
'Rand Merchant Bank', 'Banca Carige', 'World Bank', 'Bank of America', 'Deutsche Bank', 'HSBC', 'JP Morgan',
'Credit Suisse', 'JPMorgan', 'BNP Paribas', 'Goldman Sachs', 'Commerzbank', 'Deutsche Boerse', 'Handelsblatt',
'Sky News', 'Labour', 'UN', 'Bank of Japan', 'Goldman', 'Goldman Sachs Asset Management', 'New York Times',
'Bank of Scotland','World Economic Forum','Organisation for Economic Cooperation and Development',
'Russell Investments','Royal London Asset Management','Conservative party','Blom Bank','Banco Santander',
'Guardian Money','Financial Services Agency','Munich Re','Banca Popolare di Vicenza','SoftBank',
'Financial Conduct Authority','Qatar National Bank','Welt am Sonntag','Sueddeutsche Zeitung','Der Spiegel',
'Bank of England', 'Bank of America Merrill Lynch', 'Barclays', 'London Metal Exchange', 'Petroleum Exporting Countries']
for k, v in dict.items():
for org in black_list:
if org in v:
v.remove(org)
# save new dict
with open('../obj/'+ 'dict_articles_organizations_without_banks' + '.pkl', 'wb') as f:
pickle.dump(dict, f, pickle.HIGHEST_PROTOCOL)
if __name__ == '__main__':
print('# starting NER...')
print()
# read data set
file = '..\\data\\cleaned_data_set_without_header.csv'
df = pd.read_csv(file,
delimiter='|',
header=None,
index_col=None,
engine='python',
# usecols=[1,2],
# nrows=100,
quoting=csv.QUOTE_NONNUMERIC,
quotechar='\'')
#print(df)
texts = df[1] + '. ' + df[2]
NER.make_article_orgs_dict(texts)
# print('# starting NER...')
# print()
# # read data set
# file = '..\\data\\cleaned_data_set_without_header.csv'
# df = pd.read_csv(file,
# delimiter='|',
# header=None,
# index_col=None,
# engine='python',
# # usecols=[1,2],
# # nrows=100,
# quoting=csv.QUOTE_NONNUMERIC,
# quotechar='\'')
# #print(df)
# texts = df[1] + '. ' + df[2]
# NER.make_article_orgs_dict(texts)
# NER.show_most_common_companies()
# print(NER.tag_words('On Monday, Github and Microsoft announced their merger.'))
# print(NER.tag_words('On Monday, Github and Microsoft announced their merger.'))
NER.remove_banks_from_dict()