file_name = '/home/momo/Dropbox/parse_trees/' + file_name
#file_name = "./"+file_name
"""
try:
tree = pp.parse_sentence(s,'stat')
#numerate_non_terminals(tree)
dot_code = utils.nltk_tree_to_dot(tree)
print dot_code
utils.dot_to_image(dot_code, file_name + '_stat')
print
except :
print 'cannot parse with stat'
"""
try:
tree = pp.parse_sentence(s, 'stanford')
#numerate_non_terminals(tree)
tree = tree[0]
dot_code = utils.nltk_tree_to_dot(tree)
utils.dot_to_image(dot_code, file_name + '_stanford')
#dot_code = utils.list_of_tripels_to_dot(dep)
#utils.dot_to_image(dot_code, file_name + '_dep_stanford')
print "stanford done"
except:
print 'cannot parse with stanford'
"""
try :
tree = pp.parse_sentence(s,'berkeley')
tree = tree[0]
else:
print "====================================================================="
pattern_dict = load_pattern_list()
#for i in pattern_dict.items() :
# print i
#raw_input()
#s = "The Anaconda, or Water Boa, is one the world's largest snakes, when born they can be 3 feet (1m) long."
#s = ' '.join(sys.argv[1:])
sentences = sent_tokenize(s)
for s in sentences:
count += 1
tree = pp.parse_sentence(s, parser)
tree = tree[0]
#tree = Tree('S', [Tree('NP', [Tree('NNP', ['Leon'])]), Tree('VP', [Tree('VBZ', ['hits']), Tree('NP', [Tree('NNP', ['Kai'])])]), Tree('.', ['.'])])
path = utils.get_knoex_path()
dot_code = utils.nltk_tree_to_dot(tree)
utils.dot_to_image(dot_code, 'temptree_' + str(count))
if show == 2:
os.popen('gnome-open ' + 'temptree_' + str(count) + '.png')
g, _ = match_tree(tree, pattern_dict)
graph += g
while ['', '', ''] in graph:
graph.remove(['', '', ''])
print graph
return tree.leaves()
for subtree in tree :
terminals = get_terminals(subtree,node)
if terminals != None :
return terminals
if __name__ == '__main__':
from nltk import Tree
import string
import preprocessor
tree = Tree('A',[Tree('A',['A','A']),'A'])
tree = preprocessor.parse_sentence('Leon hits Kai.')
print 'Tree:', tree
print
numerate_non_terminals(tree)
print 'Num_Tree:', tree
print
print 'NP0:', get_terminals(tree,'NP0')
print 'NP1:', get_terminals(tree,'NP1')
print 'NP2:', get_terminals(tree,'NP2')
print 'NP3:', get_terminals(tree,'NP3')
print 'NP4:', get_terminals(tree,'NP4')
print
combis = all_parsing_combinations(tree)
def match_to_joined_terminals(match, parsetree):
list_ = []
for path in match :
list_.append(join(parsetree.get_terminals(path)))
return list_
def intersect(l1,l2):
s = set(l1).intersection(set(l2))
return list(s)
if __name__=="__main__":
import preprocessor as pp
tree = pp.parse_sentence('The python hits Kai.')
tree = tree[0]
pt = ParseTree(tree)
print
for item in pt.nt_dict.items() :
print item
print
for path in pt.nodepaths :
print path
print
print 'test get_subtree'
print pt.get_subtree((1,))
print 'test get_node'
print pt.get_node((1,))
print 'test get_terminals'
else :
print "====================================================================="
pattern_dict = load_pattern_list()
#for i in pattern_dict.items() :
# print i
#raw_input()
#s = "The Anaconda, or Water Boa, is one the world's largest snakes, when born they can be 3 feet (1m) long."
#s = ' '.join(sys.argv[1:])
sentences = sent_tokenize(s)
for s in sentences:
count+=1
tree = pp.parse_sentence(s,parser)
tree = tree[0]
#tree = Tree('S', [Tree('NP', [Tree('NNP', ['Leon'])]), Tree('VP', [Tree('VBZ', ['hits']), Tree('NP', [Tree('NNP', ['Kai'])])]), Tree('.', ['.'])])
path = utils.get_knoex_path()
dot_code = utils.nltk_tree_to_dot(tree)
utils.dot_to_image(dot_code, 'temptree_'+str(count))
if show == 2:
os.popen('gnome-open ' + 'temptree_'+str(count)+'.png')
g,_ = match_tree(tree, pattern_dict)
graph += g
while ['','',''] in graph:
graph.remove(['','',''])
print graph
def find_realation(text):
# HEARST PATTERNS
hp1 = 'NP\d+ such as ((NP\d+ ,d+ )+(and |or ))?NP\d+'
def m2r_1(match,tree):
NPs = re.findall(r'NP\d+', match)
NPs = [string.join(tc.get_terminals(tree,NP)) for NP in NPs]
return [(NP,'hyponym',NPs[0]) for NP in NPs[1:]]
hp2 = 'NP0 VBZ0 NP1 \.0'
def m2r_2(match,tree):
subject = ' '.join(tc.get_terminals(tree,'NP0'))
predicate = ' '.join(tc.get_terminals(tree,'VBZ0'))
object_ = ' '.join(tc.get_terminals(tree,'NP1'))
return [(subject,predicate,object_)]
hp3 = 'NP\d+ is NP\d+'
def m2r_3(match,tree):
NNs = re.findall(r'NP\d+', match)
NNs = [string.join(tc.get_terminals(tree,NN)) for NN in NNs]
return [(NN,'hyponym',NNs[0]) for NN in NNs[1:]]
# m2r = [ m2r_1, m2r_2, m2r_3 ] # functions to map matches on relations
# pattern_list = [hp1,hp2,hp3]
m2r = [ m2r_2 ] # functions to map matches on relations
pattern_list = [hp2]
sentences = split_into_sentences(text)
# The next part is rather unclean, but it's hopefully gonna work
# Produces a string where every noun phrase is preplaced by NP1,... to NPn
# Then a regex search for hearst patterns is applied to find hyponym relations
relations = []
for s in sentences:
s = format_sentence(s)
s +=' .' # adding a fullstop in the end to satisfy the needs of stanford parser
tree = parse_sentence(s)
print(type(tree))
tc.numerate_non_terminals(tree)
combi = tc.all_parsing_combinations(tree)
combi = [string.join(c) for c in combi]
#for c in combi :
# print c
#for c in combi : print c
for i, pattern in enumerate(pattern_list) :
pattern = re.compile(pattern)
#open('combi','w').write(str(combi).replace(',','\n'))
for c in combi :
match = re.match(pattern,c)
if match :
print 'match : ', match.group(), ' --> ', c
match = match.group()
if match :
tmp = m2r[i](match,tree)
print type(tmp), ' -- ', tmp
relations.extend(tmp)
print 'relations', relations
return set(relations)
return tree.leaves()
for subtree in tree :
terminals = get_terminals(subtree,node)
if terminals != None :
return terminals
if __name__ == '__main__':
from nltk import Tree
import string
import preprocessor
tree = Tree('A',[Tree('A',['A','A']),'A'])
tree = preprocessor.parse_sentence('Leon hits Kai.')
print 'Tree:', tree
print
numerate_non_terminals(tree)
print 'Num_Tree:', tree
print
print 'NP0:', get_terminals(tree,'NP0')
print 'NP1:', get_terminals(tree,'NP1')
print 'NP2:', get_terminals(tree,'NP2')
print 'NP3:', get_terminals(tree,'NP3')
print 'NP4:', get_terminals(tree,'NP4')
print
combis = all_parsing_combinations(tree)
def find_realation(text):
# HEARST PATTERNS
hp1 = 'NP\d+ such as ((NP\d+ ,d+ )+(and |or ))?NP\d+'
def m2r_1(match,tree):
NPs = re.findall(r'NP\d+', match)
NPs = [string.join(tc.get_terminals(tree,NP)) for NP in NPs]
return [(NP,'hyponym',NPs[0]) for NP in NPs[1:]]
hp2 = 'NP0 VBZ0 NP1 \.0'
def m2r_2(match,tree):
subject = ' '.join(tc.get_terminals(tree,'NP0'))
predicate = ' '.join(tc.get_terminals(tree,'VBZ0'))
object_ = ' '.join(tc.get_terminals(tree,'NP1'))
return [(subject,predicate,object_)]
hp3 = 'NP\d+ is NP\d+'
def m2r_3(match,tree):
NNs = re.findall(r'NP\d+', match)
NNs = [string.join(tc.get_terminals(tree,NN)) for NN in NNs]
return [(NN,'hyponym',NNs[0]) for NN in NNs[1:]]
#m2r = [ m2r_1, m2r_2, m2r_3 ] # functions to map matches on relations
#pattern_list = [hp1,hp2,hp3]
m2r = [ m2r_2 ] # functions to map matches on relations
pattern_list = [hp2]
sentences = split_into_sentences(text)
# The next part is rather unclean, but it's hopefully gonna work
# Produces a string where every noun phrase is preplaced by NP1,... to NPn
# Then a regex search for hearst patterns is applied to find hyponym relations
relations = []
for s in sentences:
s = format_sentence(s)
s+=' .' # adding a fullstop in the end to satisfy the needs of stanford parser
tree = parse_sentence(s)
tc.numerate_non_terminals(tree)
combi = tc.all_parsing_combinations(tree)
combi = [string.join(c) for c in combi]
#for c in combi :
# print c
#for c in combi : print c
for i, pattern in enumerate(pattern_list) :
pattern = re.compile(pattern)
#open('combi','w').write(str(combi).replace(',','\n'))
for c in combi :
match = re.match(pattern,c)
if match :
print 'match : ', match.group(), ' --> ', c
match = match.group()
if match :
tmp = m2r[i](match,tree)
print type(tmp), ' -- ', tmp
relations.extend(tmp)
print 'relations', relations
return set(relations)
#file_name = "./"+file_name
"""
try:
tree = pp.parse_sentence(s,'stat')
#numerate_non_terminals(tree)
dot_code = utils.nltk_tree_to_dot(tree)
print dot_code
utils.dot_to_image(dot_code, file_name + '_stat')
print
except :
print 'cannot parse with stat'
"""
try :
tree = pp.parse_sentence(s,'stanford')
#numerate_non_terminals(tree)
tree = tree[0]
dot_code = utils.nltk_tree_to_dot(tree)
utils.dot_to_image(dot_code, file_name + '_stanford')
#dot_code = utils.list_of_tripels_to_dot(dep)
#utils.dot_to_image(dot_code, file_name + '_dep_stanford')
print "stanford done"
except :
print 'cannot parse with stanford'
"""
try :
