How to add extra word features other then word Embedding in Recurrent Neural Network model I am building a deep learning model for NLP. I am pretty comfortable with adding word embedding from word2vec or Glove vectors as extra word features but I wanted to add other word features like POS tag of a word, NER tag of word along with embedding as features. How can I do this. Should I give these word features by concatenating their vector with the word vectors. Or is there some other method. Please suggest.
One option is to concatenate them, the second is to treat them as separate inputs. For example Keras offers such neural model: https://keras.io/getting-started/functional-api-guide/#multi-input-and-multi-output-models