svmword2vecbert-language-modelword-embeddingelmo

Training SVM classifier (word embeddings vs. sentence embeddings)

I want to experiment with different embeddings such Word2Vec, ELMo, and BERT but I'm a little confused about whether to use the word embeddings or sentence embeddings, and why. I'm using the embeddings as features input to SVM classifier.

Thank you.

Solution

  • Though both approaches can prove efficient for different datasets, as a rule of thumb I would advice you to use word embeddings when your input is of a few words, and sentence embeddings when your input in longer (e.g. large paragraphs).