Word embedding/Word2vec for POS tagging I am building a entity detection and relation classification method using deep learning approach which requires vector representation of POS tags and entity label. I am familiar with word-embedding method but I don't know the answer of following questions:


How to convert pos tags into vector representation(say 20 dim)
How to combine both word embeddings and pos embedding together to build the classifier.


Similar question was asked in Cross validate community but I couldn't find the answer. Here is the link to question:
https://stats.stackexchange.com/questions/238016/deep-learning-word-embedding-with-parts-of-speech

Research paper link:
https://arxiv.org/abs/1601.00770
Word Embedding for Pos tags can easily be trained using pos tags sequence. There are lot of ways that you can get the trained model. I did it by gensim's word2vec api. Here is the link to it: 
https://radimrehurek.com/gensim/models/word2vec.html

Also if you want memory efficient solution, radim(creater of gensim) provides a great tutorial: https://rare-technologies.com/word2vec-tutorial/

You just need to pass pos sequence of your training data, resulting vector size, min frequency count etc. You can view api's documentation for more detail.