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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

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  • $\begingroup$ I think you can just use one-hot vector for POS tag. As for now combining, you can try multiple things like giving them as independent features or concatenating them. $\endgroup$ – Hima Varsha Jan 18 '17 at 6:07
  • $\begingroup$ There are two ways I could find for embedding POS tags: first is One-hot encoding for POS tags.. Other one is learn embedding from training data with word corresponding tag as input.. Later one I think is better as it learn context of words for the relevant tag.. Currently I am implementing the second one.. if it works I will post it.. Let me know your thoughts about it.. $\endgroup$ – Neelesh Dewangan Jan 19 '17 at 1:14
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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.

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  • $\begingroup$ Hey buddy, I know it's been 2 years but can you tell me what you mean by "pass pos sequence"? If my sentence is: "w1/t1 w2/t2 w3/t3", should I send "t1 t2 t3" as input sequence to word2vec or Glove? Or is there an easy way to do that? $\endgroup$ – stuck Jan 19 at 17:12

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