class MixedCompositionalTreeKernel(SyntacticTreeKernel):
'''
Mixed Salad Kernel 2 variation
'''
kernel_name = "mixed_salad_kernel22"
def __init__(self, lambda_):
'''
Constructor
'''
self._lambda = lambda_
self._measure = CosSimilarity()
# default one
def dot_product(self, tree1, tree2):
assert_type(tree1, SemanticTree)
assert_type(tree2, SemanticTree)
return super(MixedCompositionalTreeKernel, self).dot_product(tree1, tree2)
# new delta
def _delta(self, node1, node2, node2id1, node2id2, delta_matrix):
if (node1.is_terminal() and node2.is_terminal()
and node1._label == node2._label
and node1._word == node2._word):
delta_matrix[node2id1[node1],node2id2[node2]] = 1
elif not node1.has_same_production(node2):
if node1._label != node2._label:
delta_matrix[node2id1[node1],node2id2[node2]] = 0
else:
delta_matrix[node2id1[node1],node2id2[node2]] = self._measure.get_sim(node1._vector, node2._vector)
else:
product_children_delta = self._lambda
for i in xrange(len(node1._children)):
child1 = node1.get_child(i)
child2 = node2.get_child(i)
child_delta = delta_matrix[node2id1[child1],node2id2[child2]]
if child_delta == -1:
raise ValueError("???")
else:
product_children_delta *= (1 + child_delta)
sim_children_product = 1
for i in xrange(len(node1._children)):
child1 = node1.get_child(i)
child2 = node2.get_child(i)
sim_children_product *= self._measure.get_sim(child1._vector, child2._vector)
final_delta = (product_children_delta +
(self._measure.get_sim(node1._vector, node2._vector) -
self._lambda * sim_children_product))
delta_matrix[node2id1[node1],node2id2[node2]] = final_delta
class NaiveCompositionalSemanticTreeKernel(SyntacticTreeKernel):
"""
Mixed Salad Kernel 1
"""
kernel_name = "mixed_salad_kernel1"
NO_COMPATIBILITY = 0
LABEL_COMPATIBILITY = 1
def __init__(self, lambda_, compatibility_level=LABEL_COMPATIBILITY):
"""
Constructor
"""
self._lambda = lambda_
self._compatibility_level = compatibility_level
self._measure = CosSimilarity()
def dot_product(self, tree1, tree2):
assert_type(tree1, SemanticTree)
assert_type(tree2, SemanticTree)
return super(NaiveCompositionalSemanticTreeKernel, self).dot_product(tree1, tree2)
def _delta(self, node1, node2, node2id1, node2id2, delta_matrix):
delta = 0
if self._compatibility_level == NaiveCompositionalSemanticTreeKernel.NO_COMPATIBILITY or (
self._compatibility_level == NaiveCompositionalSemanticTreeKernel.LABEL_COMPATIBILITY
and node1._label == node2._label
):
delta = (self._lambda ** (node1.get_height() + node2.get_height())) * self._measure.get_sim(
node1._vector, node2._vector
)
delta_matrix[node2id1[node1], node2id2[node2]] = delta
class SentenceVectorKernel(Kernel):
'''
classdocs
'''
kernel_name = "sentence_vector_kernel"
def __init__(self, similarity=None):
'''
Constructor
'''
if similarity is None:
self._similarity = CosSimilarity()
else:
self._similarity = similarity
def dot_product(self, tree1, tree2):
assert_type(tree1, SemanticTree)
assert_type(tree2, SemanticTree)
sentence_vector1 = tree1._root._vector
sentence_vector2 = tree2._root._vector
if sentence_vector1.norm() == 0.0 or sentence_vector2.norm() == 0.0:
return 0.0
else:
return self._similarity.get_sim(sentence_vector1, sentence_vector2)
if antonyms is not None and replacement[:-2] in antonyms:
continue
try:
replacement_vector = final_model.get_row(replacement)
except Exception, ex:
continue
if add:
context_repl_vector = base_unison + replacement_vector
else:
context_repl_vector = base_unison.multiply(replacement_vector) if base_unison is not None else replacement_vector
results[replacement] = cos_sim.get_sim(context_word_vector, context_repl_vector)
wnl = WordNetLemmatizer()
#############################################################################
# This approach was to take similar words from similarity space and then
# find synsets with the highest average replacement.
#############################################################################
if enable_synset_avg:
synsets = wn.synsets(replacement[:-2])
results_map = {}
for synset in synsets:
postag_list = get_wordnet_pos(replacement[-1].upper())
if synset.pos in postag_list:
synset_syns = synset.lemma_names
avg = 0
count = 0
