NLP文本多标签分类---HierarchicalAttentionNetwork

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最近一直在做多标签分类任务，学习了一种层次注意力模型，基本结构如下：

简单说，就是两层attention机制，一层基于词，一层基于句。

首先是词层面：

输入采用word2vec形成基本语料向量后，采用双向GRU抽特征：

一句话中的词对于当前分类的重要性不同，采用attention机制实现如下：

tensorflow代码实现如下：

···

def attention_word_level(self, hidden_state):

""" input1:self.hidden_state: hidden_state:list,len:sentence_length,element:[batch_size*num_sentences,hidden_size*2] input2:sentence level context vector:[batch_size*num_sentences,hidden_size*2] :return:representation.shape:[batch_size*num_sentences,hidden_size*2] """ hidden_state_ = tf.stack(hidden_state, axis=1) # shape:[batch_size*num_sentences,sequence_length,hidden_size*2] # 0) one layer of feed forward network hidden_state_2 = tf.reshape(hidden_state_, shape=[-1, self.hidden_size * 2]) # shape:[batch_size*num_sentences*sequence_length,hidden_size*2] # hidden_state_:[batch_size*num_sentences*sequence_length,hidden_size*2];W_w_attention_sentence:[,hidden_size*2,,hidden_size*2] hidden_representation = tf.nn.tanh(tf.matmul(hidden_state_2, self.W_w_attention_word) + self.W_b_attention_word) # shape:[batch_size*num_sentences*sequence_length,hidden_size*2] hidden_representation = tf.reshape(hidden_representation, shape=[-1, self.sequence_length, self.hidden_size * 2]) # shape:[batch_size*num_sentences,sequence_length,hidden_size*2] # attention process:1.get logits for each word in the sentence. 2.get possibility distribution for each word in the sentence. 3.get weighted sum for the sentence as sentence representation. # 1) get logits for each word in the sentence. hidden_state_context_similiarity = tf.multiply(hidden_representation, self.context_vecotor_word) # shape:[batch_size*num_sentences,sequence_length,hidden_size*2] attention_logits = tf.reduce_sum(hidden_state_context_similiarity, axis=2) # shape:[batch_size*num_sentences,sequence_length] # subtract max for numerical stability (softmax is shift invariant). tf.reduce_max:Computes the maximum of elements across dimensions of a tensor. attention_logits_max = tf.reduce_max(attention_logits, axis=1, keep_dims=True) # shape:[batch_size*num_sentences,1] # 2) get possibility distribution for each word in the sentence. p_attention = tf.nn.softmax( attention_logits - attention_logits_max) # shape:[batch_size*num_sentences,sequence_length] # 3) get weighted hidden state by attention vector p_attention_expanded = tf.expand_dims(p_attention, axis=2) # shape:[batch_size*num_sentences,sequence_length,1] # below sentence_representation'shape:[batch_size*num_sentences,sequence_length,hidden_size*2]<----p_attention_expanded:[batch_size*num_sentences,sequence_length,1];hidden_state_:[batch_size*num_sentences,sequence_length,hidden_size*2] sentence_representation = tf.multiply(p_attention_expanded, hidden_state_) # shape:[batch_size*num_sentences,sequence_length,hidden_size*2] sentence_representation = tf.reduce_sum(sentence_representation, axis=1) # shape:[batch_size*num_sentences,hidden_size*2] return sentence_representation # shape:[batch_size*num_sentences,hidden_size*2]