As a signal processing engineering and being new to NLP, I am confused with giving input to CNN network.
With my knowledge of CNN, I am trying to build a classifier for ethnicity with inputs as text (last name(LN), middle name(MN), first name(FN)). I have a list of 8,000,000 samples with last, middle, first names and class information
array = [['person1_LN','person1_MN','person1_FN','Person1_class'], ['person1_LN','person1_MN','person1_FN','Person2_class'], ....]
I want to apply conv layer (CL) followed by Pooling Layer(PL) on LN,MN,FN respectively.
Text processing example demonstrates with the sentences to convert to word embedding. I am trying to understand this code snippet
W = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), name='W') self.embedded_chars = tf.nn.embedding_lookup(W, self.input_x) self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)
This tutorial says training to be done using word2vec. Reading these two blogs I could understand nothing. In Either of the cases, my data is not a sentence, moreover in the second case on what things I have to train?
Will the CL operate if I directly give the input of words without word embeddings? If not any example on how to embed the words in my case to give the input to CNN?