def rungraphorg(line,display_name='SemanticNet'):
semantic_net = Graph()
line = np.array(pd.Series(line).reset_index())
for node2node in line:
semantic_net.add_edge(node2node[0], node2node[2], type=node2node[1],weight = node2node[3])
semantic_net.export(display_name, directed=True, weighted=0.3)
webbrowser.open('file://' + os.path.realpath(display_name+'/index.html'))
def rungraph3(line,display_name='SemanticNet'):
semantic_net = Graph()
line = pd.Series(line).reset_index()
line.columns = ['s','v','o','weight']
s_count = line['s'].value_counts()
o_count = line['o'].value_counts()
line = np.array(line)
for node2node in line:
if (node2node[0] in total and node2node[2] in total) or (s_count[node2node[0]] > 1 and o_count[node2node[2]] > 1):
semantic_net.add_edge(node2node[0], node2node[2], type=node2node[1],weight = node2node[3])
semantic_net.export(display_name, directed=True, weighted=0.3)
webbrowser.open('file://' + os.path.realpath(display_name+'/index.html'))
best_o, best_o_dist = '', 100
for (o, o_index) in o_tuple:
o_dist = abs(best_v_index - o_index)
if o_dist < best_o_dist:
best_o = o
best_o_dist = o_dist
best_svo.append((s, best_v, best_o))
return best_svo
# ***** Main Functions for Creating Semantic Network in English ***** #
semantic_net = Graph()
nlp = spacy.load("en_core_web_sm")
SUBJ = ["nsubj", "nsubjpass"]
VERB = ["ROOT"]
OBJ = ["dobj", "pobj", "dobj"]
modifier = [
'amod', 'nounmod', 'nummod', 'quantmod', 'compound', 'relcl', 'neg',
'advmod'
]
stopwords = open('/users/emilywang/shihao yang/stopword.txt').read().split(
'\n') # 自定义去除词库
stopwords.append("\n")
text = open('/users/emilywang/shihao yang/beef_english.txt').read()