I have been seeing that word embedding features (e.g. here or there) are used on classification or regression tasks where the classifier/regressor is a linear one: e.g. Linear/Logistic Regressor or GBM. However, I cannot really understand how these regressors/classifiers should interpret the word embedding dimensions and be able to capture the cosine relationship between these dimensions. Can anyone recommend a paper/blog post that can answer my question?


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.