words2 = self.words_around_symbol(candidate)
self.words2 += len(words1)
cm = CandidateMetrics()
cm.score = self.words_similarity(words1, words2, exclude)
cm.degree = ksyn.degree(self.hg, candidate)
logging.info('%s %s' % (candidate, cm))
if cm.better_than(best_cm):
best_cm = cm
best = candidate
self.best_sense_t += time.time() - start
return best, best_cm
if __name__ == '__main__':
hgr = hyperg.HyperGraph({
'backend': 'leveldb',
'hg': 'wordnet_wikidata.hg'
})
p = par.Parser()
d = Disambiguation(hgr, p)
r1 = ['stocks', 'stock']
text1 = "Chinese stocks end year with double-digit losses"
r2 = ['cambridge']
text2 = "Cambridge near Boston in the United States."
text3 = "Cambridge near London in England."
print(d.best_sense(r2, text2))
output = last_stage_output.tree.to_hyperedge_str(
with_namespaces=self.show_namespaces)
self.outputs.append(output)
self.debug_msg(output)
last_stage_output.main_edge = last_stage_output.tree.to_hyperedge()
return last_stage_output
if __name__ == '__main__':
# test_text = "Due to its location in the European Plain, Berlin is influenced by a temperate seasonal climate."
# test_text = "Lots of cars require lots of paved roadways and parking lots."
# test_text = "Critics have pointed out the dangers of group forming among like-minded in Internet. "
# test_text = "Recently online platforms such as Facebook and Google have been criticized."
# test_text = "Koikuchi shoyu, best known as soy sauce, is the mother of all sauces in Japan."
test_text = "Satellites from NASA and other agencies have been tracking sea ice changes since 1979."
print(test_text)
hgraph = hyperg.HyperGraph({
'backend': 'leveldb',
'hg': 'wordnet_dbpedia.hg'
})
extractor = Extractor(hgraph)
extractor.debug = True
results = extractor.read_text(test_text)
for result in results:
print('result: %s' % str(result[1].main_edge))
for edge in result[1].edges:
print('extra edge: %s' % str(edge))
'worst_sim': e[1][0],
'sim': e[1][1],
'matches': e[1][2],
'text': self.hg.get_str_attribute(ed.str2edge(e[0]), 'text')
}
result.append(edge_data)
return result
def write_similar_edges(self, targ_edge, file_path):
edge_data = self.similar_edges(targ_edge)
write_edge_data(edge_data, file_path)
def write_edges_with_similar_concepts(self, targ_edge, file_path):
edge_data = self.edges_with_similar_concepts(targ_edge)
write_edge_data(edge_data, file_path)
if __name__ == '__main__':
hgr = hyperg.HyperGraph({'backend': 'leveldb', 'hg': 'reddit-politics.hg'})
print('creating parser...')
par = par.Parser()
print('parser created.')
te = '(clinches/nlp.clinch.verb clinton/nlp.clinton.noun ' \
'(+/gb democratic/nlp.democratic.adj nomination/nlp.nomination.noun))'
s = Similarity(hgr, par)
# s.write_edges_with_similar_concepts(ed.str2edge(te), 'edges_similar_concepts.json')
s.write_similar_edges(ed.str2edge(te), 'similar_edges.json')
def setUp(self):
params = {'backend': 'leveldb',
'hg': 'test.hg'}
self.hg = hyperg.HyperGraph(params)
def nsimilarity(self, edges1, edges2):
cs1 = set()
for edge in edges1:
cs1 = cs1.union(self.concept_sphere(edge))
cs2 = set()
for edge in edges2:
cs2 = cs2.union(self.concept_sphere(edge))
return self.setsimilarity(cs1, cs2)
def synonym_similarity(self, meronomy, syn_id_1, syn_id_2):
return self.nsimilarity(meronomy.synonym_full_edges(syn_id_1),
meronomy.synonym_full_edges(syn_id_2))
if __name__ == '__main__':
hgr = hyperg.HyperGraph({
'backend': 'leveldb',
'hg': 'reddit-worldnews-01012013-01082017.hg'
})
hs = HyperSimilarity(hgr)
# e = 'clinton/nlp.clinton.noun'
print('starting...')
e1 = '(+/gb prime/nlp.prime.adj minister/nlp.minister.noun)'
e2 = 'europe/nlp.europe.noun'
print(hs.similarity(e1, e2))