def SimpleQ_matches_Triple(ent_char_ids_f,ent_lens_f,rel_word_ids_f,rel_word_lens_f,desH_word_ids_f,
desH_word_lens_f,
men_char_ids_f, q_word_ids_f, men_lens_f, q_word_lens_f):
# rng = numpy.random.RandomState(23455)
ent_char_input = char_embeddings[ent_char_ids_f.flatten()].reshape((batch_size,max_char_len, char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
men_char_input = char_embeddings[men_char_ids_f.flatten()].reshape((batch_size,max_char_len, char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
rel_word_input = embeddings[rel_word_ids_f.flatten()].reshape((batch_size,max_relation_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
desH_word_input = embeddings[desH_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# desT_word_input = embeddings[desT_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
q_word_input = embeddings[q_word_ids_f.flatten()].reshape((batch_size,max_Q_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#ent_mention
ent_char_conv = Conv_with_input_para(rng, input=ent_char_input,
image_shape=(batch_size, 1, char_emb_size, max_char_len),
filter_shape=char_filter_shape, W=char_conv_W, b=char_conv_b)
men_char_conv = Conv_with_input_para(rng, input=men_char_input,
image_shape=(batch_size, 1, char_emb_size, max_char_len),
filter_shape=char_filter_shape, W=char_conv_W, b=char_conv_b)
#q-rel
q_rel_conv = Conv_with_input_para(rng, input=q_word_input,
image_shape=(batch_size, 1, emb_size, max_Q_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
rel_conv = Conv_with_input_para(rng, input=rel_word_input,
image_shape=(batch_size, 1, emb_size, max_relation_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
#q_desH
q_desH_conv = Conv_with_input_para(rng, input=q_word_input,
image_shape=(batch_size, 1, emb_size, max_Q_len),
filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
desH_conv = Conv_with_input_para(rng, input=desH_word_input,
image_shape=(batch_size, 1, emb_size, max_des_len),
filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
ent_conv_pool=Max_Pooling(rng, input_l=ent_char_conv.output, left_l=ent_lens_f[0], right_l=ent_lens_f[2])
men_conv_pool=Max_Pooling(rng, input_l=men_char_conv.output, left_l=men_lens_f[0], right_l=men_lens_f[2])
#q_rel_pool=Max_Pooling(rng, input_l=q_rel_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
rel_conv_pool=Max_Pooling(rng, input_l=rel_conv.output, left_l=rel_word_lens_f[0], right_l=rel_word_lens_f[2])
q_rel_pool=Average_Pooling_for_SimpleQA(rng, input_l=q_rel_conv.output, input_r=rel_conv_pool.output_maxpooling,
left_l=q_word_lens_f[0], right_l=q_word_lens_f[2], length_l=q_word_lens_f[1]+filter_size[1]-1,
dim=max_Q_len+filter_size[1]-1, topk=2)
q_desH_pool=Max_Pooling(rng, input_l=q_desH_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
desH_conv_pool=Max_Pooling(rng, input_l=desH_conv.output, left_l=desH_word_lens_f[0], right_l=desH_word_lens_f[2])
overall_simi=cosine(ent_conv_pool.output_maxpooling, men_conv_pool.output_maxpooling)*0.33333+\
cosine(q_rel_pool.output_maxpooling, rel_conv_pool.output_maxpooling)*0.45+\
0.0*cosine(q_desH_pool.output_maxpooling, desH_conv_pool.output_maxpooling)
# cosine(q_desT_pool.output_maxpooling, desT_conv_pool.output_maxpooling)
return overall_simi
def SimpleQ_matches_Triple(rel_word_ids_f, rel_word_lens_f):
rel_word_input = embeddings[rel_word_ids_f.flatten()].reshape(
(batch_size, max_relation_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
q_word_input = embeddings[q_word_ids_f.flatten()].reshape(
(batch_size, max_Q_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#q-rel
q_rel_conv = Conv_with_input_para(rng,
input=q_word_input,
image_shape=(batch_size, 1, emb_size,
max_Q_len),
filter_shape=word_filter_shape,
W=q_rel_conv_W,
b=q_rel_conv_b)
rel_conv = Conv_with_input_para(rng,
input=rel_word_input,
image_shape=(batch_size, 1, emb_size,
max_relation_len),
filter_shape=word_filter_shape,
W=q_rel_conv_W,
b=q_rel_conv_b)
# q_rel_pool=Max_Pooling(rng, input_l=q_rel_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
rel_conv_pool = Max_Pooling(rng,
input_l=rel_conv.output,
left_l=rel_word_lens_f[0],
right_l=rel_word_lens_f[2])
q_rel_pool = Average_Pooling_for_SimpleQA(
rng,
input_l=q_rel_conv.output,
input_r=rel_conv_pool.output_maxpooling,
left_l=q_word_lens_f[0],
right_l=q_word_lens_f[2],
length_l=q_word_lens_f[1] + filter_size[1] - 1,
dim=max_Q_len + filter_size[1] - 1,
topk=2)
overall_simi = cosine(q_rel_pool.output_maxpooling,
rel_conv_pool.output_maxpooling)
return overall_simi
def SimpleQ_matches_Triple(rel_word_ids_f,rel_word_lens_f):
rel_word_input = embeddings[rel_word_ids_f.flatten()].reshape((batch_size,max_relation_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
q_word_input = embeddings[q_word_ids_f.flatten()].reshape((batch_size,max_Q_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#q-rel
q_rel_conv = Conv_with_input_para(rng, input=q_word_input,
image_shape=(batch_size, 1, emb_size, max_Q_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
rel_conv = Conv_with_input_para(rng, input=rel_word_input,
image_shape=(batch_size, 1, emb_size, max_relation_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
# q_rel_pool=Max_Pooling(rng, input_l=q_rel_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
rel_conv_pool=Max_Pooling(rng, input_l=rel_conv.output, left_l=rel_word_lens_f[0], right_l=rel_word_lens_f[2])
q_rel_pool=Average_Pooling_for_SimpleQA(rng, input_l=q_rel_conv.output, input_r=rel_conv_pool.output_maxpooling,
left_l=q_word_lens_f[0], right_l=q_word_lens_f[2], length_l=q_word_lens_f[1]+filter_size[1]-1,
dim=max_Q_len+filter_size[1]-1, topk=2)
overall_simi=cosine(q_rel_pool.output_maxpooling, rel_conv_pool.output_maxpooling)
return overall_simi
def SimpleQ_matches_Triple(ent_char_ids_f, ent_lens_f, rel_word_ids_f,
rel_word_lens_f, men_char_ids_f, q_word_ids_f,
men_lens_f, q_word_lens_f):
# rng = numpy.random.RandomState(23455)
ent_char_input = char_embeddings[ent_char_ids_f.flatten()].reshape(
(batch_size, max_char_len,
char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
men_char_input = char_embeddings[men_char_ids_f.flatten()].reshape(
(batch_size, max_char_len,
char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
rel_word_input = embeddings[rel_word_ids_f.flatten()].reshape(
(batch_size, max_relation_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#desH_word_input = embeddings[desH_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# desT_word_input = embeddings[desT_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
q_word_input = embeddings[q_word_ids_f.flatten()].reshape(
(batch_size, max_Q_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#ent_mention
ent_char_conv = Conv_with_input_para(rng,
input=ent_char_input,
image_shape=(batch_size, 1,
char_emb_size,
max_char_len),
filter_shape=char_filter_shape,
W=char_conv_W,
b=char_conv_b)
men_char_conv = Conv_with_input_para(rng,
input=men_char_input,
image_shape=(batch_size, 1,
char_emb_size,
max_char_len),
filter_shape=char_filter_shape,
W=char_conv_W,
b=char_conv_b)
#q-rel
q_rel_conv = Conv_with_input_para(rng,
input=q_word_input,
image_shape=(batch_size, 1, emb_size,
max_Q_len),
filter_shape=word_filter_shape,
W=q_rel_conv_W,
b=q_rel_conv_b)
rel_conv = Conv_with_input_para(rng,
input=rel_word_input,
image_shape=(batch_size, 1, emb_size,
max_relation_len),
filter_shape=word_filter_shape,
W=q_rel_conv_W,
b=q_rel_conv_b)
#q_desH
#q_desH_conv = Conv_with_input_para(rng, input=q_word_input,
# image_shape=(batch_size, 1, emb_size, max_Q_len),
# filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
#desH_conv = Conv_with_input_para(rng, input=desH_word_input,
# image_shape=(batch_size, 1, emb_size, max_des_len),
# filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
ent_conv_pool = Max_Pooling(rng,
input_l=ent_char_conv.output,
left_l=ent_lens_f[0],
right_l=ent_lens_f[2])
men_conv_pool = Max_Pooling(rng,
input_l=men_char_conv.output,
left_l=men_lens_f[0],
right_l=men_lens_f[2])
#q_rel_pool=Max_Pooling(rng, input_l=q_rel_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
rel_conv_pool = Max_Pooling(rng,
input_l=rel_conv.output,
left_l=rel_word_lens_f[0],
right_l=rel_word_lens_f[2])
q_rel_pool = Average_Pooling_for_SimpleQA(
rng,
input_l=q_rel_conv.output,
input_r=rel_conv_pool.output_maxpooling,
left_l=q_word_lens_f[0],
right_l=q_word_lens_f[2],
length_l=q_word_lens_f[1] + filter_size[1] - 1,
dim=max_Q_len + filter_size[1] - 1,
topk=2)
#q_desH_pool=Max_Pooling(rng, input_l=q_desH_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
#desH_conv_pool=Max_Pooling(rng, input_l=desH_conv.output, left_l=desH_word_lens_f[0], right_l=desH_word_lens_f[2])
overall_simi=cosine(ent_conv_pool.output_maxpooling, men_conv_pool.output_maxpooling)*0.33333+\
cosine(q_rel_pool.output_maxpooling, rel_conv_pool.output_maxpooling)*0.55
# 0.0*cosine(q_desH_pool.output_maxpooling, desH_conv_pool.output_maxpooling)
# cosine(q_desT_pool.output_maxpooling, desT_conv_pool.output_maxpooling)
return overall_simi
def SimpleQ_matches_Triple(ent_char_ids_f,ent_lens_f,rel_word_ids_f,rel_word_lens_f,
men_char_ids_f, q_word_ids_f, men_lens_f, q_word_lens_f):#, ent_score):
# rng = numpy.random.RandomState(23455)
ent_char_input = char_embeddings[ent_char_ids_f.flatten()].reshape((batch_size,max_char_len, char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
men_char_input = char_embeddings[men_char_ids_f.flatten()].reshape((batch_size,max_char_len, char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
rel_word_input = embeddings[rel_word_ids_f.flatten()].reshape((batch_size,max_relation_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#desH_word_input = embeddings[desH_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
# desT_word_input = embeddings[desT_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
q_word_input = embeddings[q_word_ids_f.flatten()].reshape((batch_size,max_Q_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#ent_mention
ent_char_conv = Conv_with_input_para(rng, input=ent_char_input,
image_shape=(batch_size, 1, char_emb_size, max_char_len),
filter_shape=char_filter_shape, W=char_conv_W, b=char_conv_b)
men_char_conv = Conv_with_input_para(rng, input=men_char_input,
image_shape=(batch_size, 1, char_emb_size, max_char_len),
filter_shape=char_filter_shape, W=char_conv_W, b=char_conv_b)
#q-rel
q_rel_conv = Conv_with_input_para(rng, input=q_word_input,
image_shape=(batch_size, 1, emb_size, max_Q_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
rel_conv = Conv_with_input_para(rng, input=rel_word_input,
image_shape=(batch_size, 1, emb_size, max_relation_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
#q_desH
#q_desH_conv = Conv_with_input_para(rng, input=q_word_input,
# image_shape=(batch_size, 1, emb_size, max_Q_len),
# filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
#desH_conv = Conv_with_input_para(rng, input=desH_word_input,
# image_shape=(batch_size, 1, emb_size, max_des_len),
# filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
# #q_desT
# q_desT_conv = Conv_with_input_para(rng, input=q_word_input,
# image_shape=(batch_size, 1, emb_size, max_Q_len),
# filter_shape=word_filter_shape, W=q_desT_conv_W, b=q_desT_conv_b)
# desT_conv = Conv_with_input_para(rng, input=desT_word_input,
# image_shape=(batch_size, 1, emb_size, max_des_len),
# filter_shape=word_filter_shape, W=q_desT_conv_W, b=q_desT_conv_b)
# ent_char_output=debug_print(ent_char_conv.output, 'ent_char.output')
# men_char_output=debug_print(men_char_conv.output, 'men_char.output')
ent_conv_pool=Max_Pooling(rng, input_l=ent_char_conv.output, left_l=ent_lens_f[0], right_l=ent_lens_f[2])
men_conv_pool=Max_Pooling(rng, input_l=men_char_conv.output, left_l=men_lens_f[0], right_l=men_lens_f[2])
# q_rel_pool=Max_Pooling(rng, input_l=q_rel_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
rel_conv_pool=Max_Pooling(rng, input_l=rel_conv.output, left_l=rel_word_lens_f[0], right_l=rel_word_lens_f[2])
q_rel_pool=Average_Pooling_for_SimpleQA(rng, input_l=q_rel_conv.output, input_r=rel_conv_pool.output_maxpooling,
left_l=q_word_lens_f[0], right_l=q_word_lens_f[2], length_l=q_word_lens_f[1]+filter_size[1]-1,
dim=max_Q_len+filter_size[1]-1, topk=2)
#q_desH_pool=Max_Pooling(rng, input_l=q_desH_conv.output, left_l=q_word_lens_f[0], right_l=q_word_lens_f[2])
#desH_conv_pool=Max_Pooling(rng, input_l=desH_conv.output, left_l=desH_word_lens_f[0], right_l=desH_word_lens_f[2])
# q_desT_pool=Max_Pooling(rng, input_l=q_desT_conv.output, left_l=q_word_lens[0], right_l=q_word_lens[2])
# desT_conv_pool=Max_Pooling(rng, input_l=desT_conv.output, left_l=desT_word_lens_f[0], right_l=desT_word_lens_f[2])
overall_simi=cosine(ent_conv_pool.output_maxpooling, men_conv_pool.output_maxpooling)*0.3333+\
cosine(q_rel_pool.output_maxpooling, rel_conv_pool.output_maxpooling)*0.3333*1.35
#0.000001*cosine(q_desH_pool.output_maxpooling, desH_conv_pool.output_maxpooling)
#simi_vector=T.concatenate([cosine(ent_conv_pool.output_maxpooling, men_conv_pool.output_maxpooling).reshape((1,1)),
# cosine(q_rel_pool.output_maxpooling, rel_conv_pool.output_maxpooling).reshape((1,1)),
# cosine(q_desH_pool.output_maxpooling, desH_conv_pool.output_maxpooling).reshape((1,1)),
# ent_score.reshape((1,1))], axis=1)
#len=T.sqrt(T.sum(simi_vector**2))
#normed_simi=simi_vector/len
#overall_simi=T.tanh(T.sum(normed_simi*simi_comb_weights))
# cosine(q_desT_pool.output_maxpooling, desT_conv_pool.output_maxpooling)
return overall_simi
def SimpleQ_matches_Triple(ent_char_ids_f,ent_lens_f,rel_word_ids_f,rel_word_lens_f,desH_word_ids_f,
desH_word_lens_f,desT_word_ids_f,desT_word_lens_f):
# rng = numpy.random.RandomState(23455)
ent_char_input = char_embeddings[ent_char_ids_f.flatten()].reshape((batch_size,max_char_len, char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
men_char_input = char_embeddings[men_char_ids.flatten()].reshape((batch_size,max_char_len, char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
rel_word_input = embeddings[rel_word_ids_f.flatten()].reshape((batch_size,max_relation_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
desH_word_input = embeddings[desH_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
desT_word_input = embeddings[desT_word_ids_f.flatten()].reshape((batch_size,max_des_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
q_word_input = embeddings[q_word_ids.flatten()].reshape((batch_size,max_Q_len, emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#ent_mention
ent_char_conv = Conv_with_input_para(rng, input=ent_char_input,
image_shape=(batch_size, 1, char_emb_size, max_char_len),
filter_shape=char_filter_shape, W=char_conv_W, b=char_conv_b)
men_char_conv = Conv_with_input_para(rng, input=men_char_input,
image_shape=(batch_size, 1, char_emb_size, max_char_len),
filter_shape=char_filter_shape, W=char_conv_W, b=char_conv_b)
#q-rel
q_rel_conv = Conv_with_input_para(rng, input=q_word_input,
image_shape=(batch_size, 1, emb_size, max_Q_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
rel_conv = Conv_with_input_para(rng, input=rel_word_input,
image_shape=(batch_size, 1, emb_size, max_relation_len),
filter_shape=word_filter_shape, W=q_rel_conv_W, b=q_rel_conv_b)
#q_desH
q_desH_conv = Conv_with_input_para(rng, input=q_word_input,
image_shape=(batch_size, 1, emb_size, max_Q_len),
filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
desH_conv = Conv_with_input_para(rng, input=desH_word_input,
image_shape=(batch_size, 1, emb_size, max_des_len),
filter_shape=word_filter_shape, W=q_desH_conv_W, b=q_desH_conv_b)
#q_desT
q_desT_conv = Conv_with_input_para(rng, input=q_word_input,
image_shape=(batch_size, 1, emb_size, max_Q_len),
filter_shape=word_filter_shape, W=q_desT_conv_W, b=q_desT_conv_b)
desT_conv = Conv_with_input_para(rng, input=desT_word_input,
image_shape=(batch_size, 1, emb_size, max_des_len),
filter_shape=word_filter_shape, W=q_desT_conv_W, b=q_desT_conv_b)
# ent_char_output=debug_print(ent_char_conv.output, 'ent_char.output')
# men_char_output=debug_print(men_char_conv.output, 'men_char.output')
ent_conv_pool=Max_Pooling(rng, input_l=ent_char_conv.output, left_l=ent_lens_f[0], right_l=ent_lens_f[2])
men_conv_pool=Max_Pooling(rng, input_l=men_char_conv.output, left_l=men_lens[0], right_l=men_lens[2])
q_rel_pool=Max_Pooling(rng, input_l=q_rel_conv.output, left_l=q_word_lens[0], right_l=q_word_lens[2])
rel_conv_pool=Max_Pooling(rng, input_l=rel_conv.output, left_l=rel_word_lens_f[0], right_l=rel_word_lens_f[2])
q_desH_pool=Max_Pooling(rng, input_l=q_desH_conv.output, left_l=q_word_lens[0], right_l=q_word_lens[2])
desH_conv_pool=Max_Pooling(rng, input_l=desH_conv.output, left_l=desH_word_lens_f[0], right_l=desH_word_lens_f[2])
q_desT_pool=Max_Pooling(rng, input_l=q_desT_conv.output, left_l=q_word_lens[0], right_l=q_word_lens[2])
desT_conv_pool=Max_Pooling(rng, input_l=desT_conv.output, left_l=desT_word_lens_f[0], right_l=desT_word_lens_f[2])
overall_simi=cosine(ent_conv_pool.output_maxpooling, men_conv_pool.output_maxpooling)+\
cosine(q_rel_pool.output_maxpooling, rel_conv_pool.output_maxpooling)+\
cosine(q_desH_pool.output_maxpooling, desH_conv_pool.output_maxpooling)+\
cosine(q_desT_pool.output_maxpooling, desT_conv_pool.output_maxpooling)
return overall_simi
def SimpleQ_matches_Triple(ent_char_ids_f, ent_lens_f, rel_word_ids_f,
rel_word_lens_f, desH_word_ids_f,
desH_word_lens_f, desT_word_ids_f,
desT_word_lens_f):
# rng = numpy.random.RandomState(23455)
ent_char_input = char_embeddings[ent_char_ids_f.flatten()].reshape(
(batch_size, max_char_len,
char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
men_char_input = char_embeddings[men_char_ids.flatten()].reshape(
(batch_size, max_char_len,
char_emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
rel_word_input = embeddings[rel_word_ids_f.flatten()].reshape(
(batch_size, max_relation_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
desH_word_input = embeddings[desH_word_ids_f.flatten()].reshape(
(batch_size, max_des_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
desT_word_input = embeddings[desT_word_ids_f.flatten()].reshape(
(batch_size, max_des_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
q_word_input = embeddings[q_word_ids.flatten()].reshape(
(batch_size, max_Q_len,
emb_size)).transpose(0, 2, 1).dimshuffle(0, 'x', 1, 2)
#ent_mention
ent_char_conv = Conv_with_input_para(rng,
input=ent_char_input,
image_shape=(batch_size, 1,
char_emb_size,
max_char_len),
filter_shape=char_filter_shape,
W=char_conv_W,
b=char_conv_b)
men_char_conv = Conv_with_input_para(rng,
input=men_char_input,
image_shape=(batch_size, 1,
char_emb_size,
max_char_len),
filter_shape=char_filter_shape,
W=char_conv_W,
b=char_conv_b)
#q-rel
q_rel_conv = Conv_with_input_para(rng,
input=q_word_input,
image_shape=(batch_size, 1, emb_size,
max_Q_len),
filter_shape=word_filter_shape,
W=q_rel_conv_W,
b=q_rel_conv_b)
rel_conv = Conv_with_input_para(rng,
input=rel_word_input,
image_shape=(batch_size, 1, emb_size,
max_relation_len),
filter_shape=word_filter_shape,
W=q_rel_conv_W,
b=q_rel_conv_b)
#q_desH
q_desH_conv = Conv_with_input_para(rng,
input=q_word_input,
image_shape=(batch_size, 1,
emb_size, max_Q_len),
filter_shape=word_filter_shape,
W=q_desH_conv_W,
b=q_desH_conv_b)
desH_conv = Conv_with_input_para(rng,
input=desH_word_input,
image_shape=(batch_size, 1, emb_size,
max_des_len),
filter_shape=word_filter_shape,
W=q_desH_conv_W,
b=q_desH_conv_b)
#q_desT
q_desT_conv = Conv_with_input_para(rng,
input=q_word_input,
image_shape=(batch_size, 1,
emb_size, max_Q_len),
filter_shape=word_filter_shape,
W=q_desT_conv_W,
b=q_desT_conv_b)
desT_conv = Conv_with_input_para(rng,
input=desT_word_input,
image_shape=(batch_size, 1, emb_size,
max_des_len),
filter_shape=word_filter_shape,
W=q_desT_conv_W,
b=q_desT_conv_b)
# ent_char_output=debug_print(ent_char_conv.output, 'ent_char.output')
# men_char_output=debug_print(men_char_conv.output, 'men_char.output')
ent_conv_pool = Max_Pooling(rng,
input_l=ent_char_conv.output,
left_l=ent_lens_f[0],
right_l=ent_lens_f[2])
men_conv_pool = Max_Pooling(rng,
input_l=men_char_conv.output,
left_l=men_lens[0],
right_l=men_lens[2])
q_rel_pool = Max_Pooling(rng,
input_l=q_rel_conv.output,
left_l=q_word_lens[0],
right_l=q_word_lens[2])
rel_conv_pool = Max_Pooling(rng,
input_l=rel_conv.output,
left_l=rel_word_lens_f[0],
right_l=rel_word_lens_f[2])
q_desH_pool = Max_Pooling(rng,
input_l=q_desH_conv.output,
left_l=q_word_lens[0],
right_l=q_word_lens[2])
desH_conv_pool = Max_Pooling(rng,
input_l=desH_conv.output,
left_l=desH_word_lens_f[0],
right_l=desH_word_lens_f[2])
q_desT_pool = Max_Pooling(rng,
input_l=q_desT_conv.output,
left_l=q_word_lens[0],
right_l=q_word_lens[2])
desT_conv_pool = Max_Pooling(rng,
input_l=desT_conv.output,
left_l=desT_word_lens_f[0],
right_l=desT_word_lens_f[2])
overall_simi=cosine(ent_conv_pool.output_maxpooling, men_conv_pool.output_maxpooling)+\
cosine(q_rel_pool.output_maxpooling, rel_conv_pool.output_maxpooling)+\
cosine(q_desH_pool.output_maxpooling, desH_conv_pool.output_maxpooling)+\
cosine(q_desT_pool.output_maxpooling, desT_conv_pool.output_maxpooling)
return overall_simi