8
$\begingroup$

I am new to topic modeling and read about LDA and NMF (Non-negative Matrix Factorization). I understand the training process work. Let's say I have 100 documents and I want to train an LDA for these documents with 10 topics. However, I don't really understand how does this model assign topic to an unseen document?

I used Gensim. After training, I have an LDA trained model and a dictionary with most frequent words. Let's say, I have an unseen new document with the following text: 

This is just a test text about topic modeling and LDA.

Can someone explain step by step how a topic distribution is assigned to this new document in terms of algorithmic steps? The same goes for NMF method.

Improve this question
$\endgroup$
5
  • $\begingroup$ By the context, I understand that LDA refers to Latent Dirichlet Allocation, but please clarify this in the question. Also include the full name for Non-negative Matrix Factorization. $\endgroup$
    – Daniel López
    Jan 29, 2018 at 14:37
  • $\begingroup$ The Bayes decision rule of assigning topics to new documents depends on the loss function. $\endgroup$
    – Łukasz Grad
    Jan 29, 2018 at 14:49
  • $\begingroup$ LDA does not assign topics to documents, it assigns topics to words and topic-distributions to documents. $\endgroup$
    – guy
    Jan 29, 2018 at 15:23
  • $\begingroup$ @guy I should have explicitly specified that. I meant topic distribution. $\endgroup$
    – nickg
    Jan 29, 2018 at 15:25
  • $\begingroup$ The topic distribution represented as a point on the $n_{topic}$-dimensional simplex, and is inferred by looking at the posterior under a Dirichlet prior. If we were to use, say, a Gibbs sampler, the topic distribution would be updated across iterations by sampling from the associated full conditional, which by conjugacy is another Dirichlet. $\endgroup$
    – guy
    Jan 29, 2018 at 17:23

1 Answer 1

Reset to default
2
$\begingroup$

What you should actually do is run inference (training) on the new set of documents (the old ones and the new ones together). A short-cut that estimates this well is applying Gibbs sampling only to the new documents while using the data obtained during training unchanged, as described by @SheldonCooper in Topic prediction using latent Dirichlet allocation.

Improve this answer
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.