100 lines
4.7 KiB
Python
100 lines
4.7 KiB
Python
'''
|
|
Named Entity Recognition (NER)
|
|
==============================
|
|
|
|
Stanford NER takes a text as input and returns a list of entities
|
|
like persons, organizations and countries, e.g.
|
|
'''
|
|
|
|
import os
|
|
|
|
import matplotlib.pyplot as plt
|
|
from nltk.tag import StanfordNERTagger
|
|
from nltk.tokenize import word_tokenize
|
|
|
|
class NER:
|
|
|
|
def tag_words(text):
|
|
stanford_classifier = 'C:\\Users\\anne.lorenz\\Bachelorarbeit\\StanfordNER\\stanford-ner-2018-02-27\\classifiers\\english.all.3class.distsim.crf.ser.gz'
|
|
stanford_ner_path = 'C:\\Users\\anne.lorenz\\Bachelorarbeit\\StanfordNER\\stanford-ner-2018-02-27\\stanford-ner.jar'
|
|
# create tagger object
|
|
st = StanfordNERTagger(stanford_classifier, stanford_ner_path, encoding='utf-8')
|
|
|
|
tokenized_text = word_tokenize(text)
|
|
tagged_words = st.tag(tokenized_text)
|
|
# returns list of tuples (word, tag)
|
|
return tagged_words
|
|
|
|
def get_coherent_names(tagged_words):
|
|
continuous_chunk = []
|
|
current_chunk = []
|
|
|
|
for token, tag in tagged_words:
|
|
if tag != "O":
|
|
current_chunk.append((token, tag))
|
|
else:
|
|
# if current chunk is not empty
|
|
if current_chunk:
|
|
continuous_chunk.append(current_chunk)
|
|
current_chunk = []
|
|
# put the final current_chunk into the continuous_chunk (if any)
|
|
if current_chunk:
|
|
continuous_chunk.append(current_chunk)
|
|
return continuous_chunk
|
|
|
|
def plot_barchart():
|
|
organizations = ['org1', 'org2', 'org3', 'org4', 'org5', 'org6']
|
|
num_mentions = [5, 2, 33, 12, 6, 10]
|
|
#n, bins, patches = plt.hist(num_mentions, 6, normed=1, facecolor='green')
|
|
plt.plot(organizations, num_mentions, 'ro', ms = 10)
|
|
plt.xlabel('companies')
|
|
plt.ylabel('count')
|
|
plt.title('Company mentions in articles')
|
|
plt.grid(True)
|
|
plt.show()
|
|
|
|
def find_companies(text):
|
|
#set paths
|
|
java_path = "C:\\Program Files (x86)\\Java\\jre1.8.0_181"
|
|
os.environ['JAVAHOME'] = java_path
|
|
|
|
organizations = []
|
|
# create list of (word, tag) tuples
|
|
tagged_words = NER.tag_words(text)
|
|
# put coherent names together
|
|
nes = NER.get_coherent_names(tagged_words)
|
|
nes_coherent = [(" ".join([token for token, tag in ne]), ne[0][1]) for ne in nes]
|
|
#print(nes_coherent)
|
|
for tuple in nes_coherent:
|
|
if tuple[1] == 'ORGANIZATION':
|
|
organizations.append(tuple[0])
|
|
return organizations
|
|
|
|
if __name__ == '__main__':
|
|
|
|
#plot_barchart()
|
|
text = '''BANGKOK, Sept 22 (Reuters) - Southeast Asian stock markets
|
|
\nmostly fell in light volumes on Tuesday as energy shares
|
|
tracked \nfalls in global oil prices, while weaknesses in banking shares
|
|
\namid concerns about loans to an ailing steel firm sent the Thai
|
|
\nindex to a one-week closing low. \nBangkok's SET index shed nearly
|
|
1 percent after four \nsessions of gains. The index closed at 1,379.32,
|
|
its lowest \nclosing since Sept. 15. \nShares of Krung Thai Bank Pcl,
|
|
the most actively \ntraded by turnover, dropped 2.8 percent to a near
|
|
one-month low, \nreflecting potential impact of loans to Sahaviriya Steel
|
|
\nIndustries Pcl on the bank's earnings. \nMaybank Kim Eng Securities
|
|
downgraded Krung Thai Bank to \n\"hold\" from \"buy\". \n\"Even as exposure
|
|
to SSI loans will be fully provisioned, \nKTB's NPL coverage will still be
|
|
lower than 130 percent, the \ndesired level we think and hence the need for
|
|
more provisioning \nin the following quarters,\" the broker said in a report.
|
|
\nSSI shares plunged 20 percent and Siam Commercial Bank \n, among its
|
|
creditors, dropped 1 percent. The steel firm \nand its three creditors
|
|
agreed on Monday to consider options to \nrestructure debt worth over
|
|
50 billion baht ($1.40 \nbillion). \nStocks in Malaysia extended their
|
|
slides for a third \nsession, Singapore gave up early gains and Indonesia
|
|
\nhit a near one-week low, all with trading volumes below \nthe 30-day
|
|
average ahead of a public holiday on Thursday. \nAmong top losers in the
|
|
region, Indonesia's Perusahaan Gas \nNegara was down 4.4 percent and
|
|
Singapore's Keppel \nCorp was down 2.5 percent as crude oil prices fell
|
|
\namid uncertainty over global demand. \nFor Asian Companies click.'''
|
|
print(NER.find_companies(text)) |