基本思路：刚开始是通过提取关键词计算jaccard相似度的方法，来得出论文对在子空间上的相似度。关键词是用多种算法提取关键词（候选词）后进行综合而得到的。后面发现使用的方法大多和语义不太相关，所以又打算使用bert进行训练子空间上的句向量，然后不是计算jaccard相似度，而是计算cos等其他的相似度。 1.LSTM实现文本相似度：
def get_model(nb_words, EMBEDDING_DIM, embedding_matrix, MAX_SEQUENCE_LENGTH, num_lstm, rate_drop_lstm, rate_drop_dense, num_dense, act): sequence_1_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32') sequence_2_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32') # embedding embedding_layer = Embedding(nb_words, EMBEDDING_DIM, weights=[embedding_matrix], input_length=MAX_SEQUENCE_LENGTH, trainable=False) embedded_sequences_1 = embedding_layer(sequence_1_input) embedded_sequences_2 = embedding_layer(sequence_2_input) # lstm lstm_layer = LSTM(num_lstm, dropout=rate_drop_lstm, recurrent_dropout=rate_drop_lstm) x1 = lstm_layer(embedded_sequences_1) y1 = lstm_layer(embedded_sequences_2) # classifier merged = concatenate([x1, y1]) merged = Dropout(rate_drop_dense)(merged) merged = BatchNormalization()(merged) merged = Dense(num_dense, activation=act)(merged) merged = Dropout(rate_drop_dense)(merged) merged = BatchNormalization()(merged) preds = Dense(1, activation='sigmoid')(merged) model = Model(inputs=[sequence_1_input, sequence_2_input], \ outputs=preds) model.compile(loss='binary_crossentropy', optimizer='nadam', metrics=['acc']) model.summary() return model BiLSTM实现文本相似度 def get_model(nb_words, EMBEDDING_DIM, embedding_matrix, MAX_SEQUENCE_LENGTH, num_lstm, rate_drop_lstm, rate_drop_dense, num_dense, act): embedding_layer = Embedding(nb_words, EMBEDDING_DIM, weights=[embedding_matrix], input_length=MAX_SEQUENCE_LENGTH, trainable=False) lstm_layer = Bidirectional(LSTM(num_lstm, dropout=rate_drop_lstm, recurrent_dropout=rate_drop_lstm)) sequence_1_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32') embedded_sequences_1 = embedding_layer(sequence_1_input) x1 = lstm_layer(embedded_sequences_1) sequence_2_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32') embedded_sequences_2 = embedding_layer(sequence_2_input) y1 = lstm_layer(embedded_sequences_2) merged = concatenate([x1, y1]) merged = Dropout(rate_drop_dense)(merged) merged = BatchNormalization()(merged) merged = Dense(num_dense, activation=act)(merged) merged = Dropout(rate_drop_dense)(merged) merged = BatchNormalization()(merged) preds = Dense(1, activation='sigmoid')(merged) model = Model(inputs=[sequence_1_input, sequence_2_input], \ outputs=preds) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['acc']) model.summary() return model3.ESIM实现文本相似度
def get_model(embedding_matrix, nb_words, EMBEDDING_DIM, MAX_SEQUENCE_LENGTH, num_lstm, rate_drop_dense): att1_layer = Attention.Attention(MAX_SEQUENCE_LENGTH) sequence_1_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32') # 编码后的问题1的词特征 sequence_2_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32') # 编码后的问题2的词特征 # embedding embedding_layer = Embedding(nb_words, EMBEDDING_DIM, weights=[embedding_matrix], input_length=MAX_SEQUENCE_LENGTH, trainable=False) embedded_sequences_1 = embedding_layer(sequence_1_input) embedded_sequences_2 = embedding_layer(sequence_2_input) # encode lstm1_layer = Bidirectional(LSTM(num_lstm)) encode_sequences_1 = lstm1_layer(embedded_sequences_1) encode_sequences_2 = lstm1_layer(embedded_sequences_2) # lstm lstm0_layer = LSTM(num_lstm, return_sequences=True) lstm2_layer = LSTM(num_lstm) v1ls = lstm2_layer(lstm0_layer(embedded_sequences_1)) v2ls = lstm2_layer(lstm0_layer(embedded_sequences_2)) v1 = Concatenate(axis=1)([att1_layer(embedded_sequences_1), encode_sequences_1]) v2 = Concatenate(axis=1)([att1_layer(embedded_sequences_2), encode_sequences_2]) # sequence_1c_input = Input(shape=(MAX_SEQUENCE_LENGTH_CHAR,), dtype='int32') # 编码后的问题1的字特征 # sequence_2c_input = Input(shape=(MAX_SEQUENCE_LENGTH_CHAR,), dtype='int32') # 编码后的问题2的字特征 # embedding_char_layer = Embedding(char_words, # EMBEDDING_DIM) # embedded_sequences_1c = embedding_char_layer(sequence_1c_input) # embedded_sequences_2c = embedding_char_layer(sequence_2c_input) # x1c = lstm1_layer(embedded_sequences_1c) # x2c = lstm1_layer(embedded_sequences_2c) # v1c = Concatenate(axis=1)([att1_layer(embedded_sequences_1c), x1c]) # v2c = Concatenate(axis=1)([att1_layer(embedded_sequences_2c), x2c]) # compose mul = Multiply()([v1, v2]) sub = Lambda(lambda x: K.abs(x))(Subtract()([v1, v2])) maximum = Maximum()([Multiply()([v1, v1]), Multiply()([v2, v2])]) # mulc = Multiply()([v1c, v2c]) # subc = Lambda(lambda x: K.abs(x))(Subtract()([v1c, v2c])) # maximumc = Maximum()([Multiply()([v1c, v1c]), Multiply()([v2c, v2c])]) sub2 = Lambda(lambda x: K.abs(x))(Subtract()([v1ls, v2ls])) # matchlist = Concatenate(axis=1)([mul, sub, mulc, subc, maximum, maximumc, sub2]) matchlist = Concatenate(axis=1)([mul, sub, maximum, sub2]) matchlist = Dropout(rate_drop_dense)(matchlist) matchlist = Concatenate(axis=1)( [Dense(32, activation='relu')(matchlist), Dense(48, activation='sigmoid')(matchlist)]) res = Dense(1, activation='sigmoid')(matchlist) # model = Model(inputs=[sequence_1_input, sequence_2_input, # sequence_1c_input, sequence_2c_input], outputs=res) model = Model(inputs=[sequence_1_input, sequence_2_input], outputs=res) model.compile(optimizer=Adam(lr=0.001), loss="binary_crossentropy", metrics=['acc']) model.summary() return model4.Decomption + Attention实现文本相似度
def get_model(embedding_matrix_file, MAX_SEQUENCE_LENGTH, rate_drop_projction, num_projction, hidden_projction, rate_drop_compare, num_compare, rate_drop_dense, num_dense): sequence_1_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32') sequence_2_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32') # embedding embedding_layer = create_pretrained_embedding(embedding_matrix_file, mask_zero=False) embedded_sequences_1 = embedding_layer(sequence_1_input) embedded_sequences_2 = embedding_layer(sequence_2_input) # projection projection_layers = [] if hidden_projction > 0: projection_layers.extend([ Dense(hidden_projction, activation='elu'), Dropout(rate=rate_drop_projction), ]) projection_layers.extend([ Dense(num_projction, activation=None), Dropout(rate=rate_drop_projction), ]) encode_sequences_1 = time_distributed(embedded_sequences_1, projection_layers) encode_sequences_2 = time_distributed(embedded_sequences_2, projection_layers) # attention alignd_sequences_1, alignd_sequences_2 = soft_attention_alignment(encode_sequences_1, encode_sequences_2) # compare combined_sequences_1 = Concatenate()( [encode_sequences_1, alignd_sequences_2, submult(encode_sequences_1, alignd_sequences_2)]) combined_sequences_2 = Concatenate()( [encode_sequences_2, alignd_sequences_1, submult(encode_sequences_2, alignd_sequences_1)]) compare_layers = [ Dense(num_compare, activation='elu'), Dropout(rate_drop_compare), Dense(num_compare, activation='elu'), Dropout(rate_drop_compare), ] compare_sequences_1 = time_distributed(combined_sequences_1, compare_layers) compare_sequences_2 = time_distributed(combined_sequences_2, compare_layers) # aggregate rep_sequences_1 = apply_multiple(compare_sequences_1, [GlobalAvgPool1D(), GlobalMaxPool1D()]) rep_sequences_2 = apply_multiple(compare_sequences_2, [GlobalAvgPool1D(), GlobalMaxPool1D()]) # classifier merged = Concatenate()([rep_sequences_1, rep_sequences_2]) dense = BatchNormalization()(merged) dense = Dense(num_dense, activation='elu')(dense) dense = Dropout(rate_drop_dense)(dense) dense = BatchNormalization()(dense) dense = Dense(num_dense, activation='elu')(dense) dense = Dropout(rate_drop_dense)(dense) out_ = Dense(1, activation='sigmoid')(dense) model = Model(inputs=[sequence_1_input, sequence_2_input], outputs=out_) model.compile(optimizer=Adam(lr=1e-3), loss='binary_crossentropy', metrics=['binary_crossentropy', 'accuracy']) return model