#lda

Question 1: Select all that apply

Which statements about word embeddings and LDA topic models are true? Select all that apply.

Word2vec embeddings can replace bag-of-words features as the input of a text classifier1
Latent Dirichlet Allocation (LDA) topic modeling is an unsupervised method2
In an LDA topic model, the same word can have a high probability in two different topics3
In an LDA topic model, each document is a mixture of several topics4
An LDA model with K = 10 trained on news articles outputs 10 topic labels such as "sports" or "politics"5