I am wondering if I am using word embeddings correctly.
I have combined contextualised word vectors with static word vectors because:
- my domain corpus is too small to effectively train the model from scratch
- my domain is too specialised to use general embeddings.
I used the off the shelf ELMo small model and trained word2vec model on a small domain specific corpus (around 500 academic papers). I then did a simple concatenation of the vectors from the two different embeddings.
I loosely followed the approach in this paper: https://www.aclweb.org/anthology/P19-2041.pdf But the approach in the paper trains the embeddings for a specific task. In my domain there is no labeled training data. Hence me just training the embeddings on the corpus alone.
I am new to NLP, so apologies if I am asking a stupid question.
question from:https://stackoverflow.com/questions/65842724/is-it-ok-to-combine-domain-specific-word2vec-embeddings-and-off-the-shelf-elmo-e
