lda2vec

pytorch implementation of Moody's lda2vec, a way of topic modeling using word embeddings.
The original paper: Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec.

Warning: I, personally, believe that it is quite hard to make lda2vec algorithm work.
Sometimes it finds a couple of topics, sometimes not. Usually a lot of found topics are a total mess.
The algorithm is prone to poor local minima. It greatly depends on values of initial topic assignments.

For my results see 20newsgroups/explore_trained_model.ipynb. Also see Implementation details below.

Loss

The training proceeds as follows. First, convert a document corpus to a set of tuples
{(document id, word, the window around the word) | for each word in the corpus}.
Second, for each tuple maximize the following objective function

where c - context vector, w - embedding vector for a word, lambda - positive constant that controls sparsity, i - sum over the window around the word, k - sum over sampled negative words, j - sum over topics, p - probability distribution over topics for a document, t - topic vectors.
When training I also shuffle and batch the tuples.

How to use it

1. Go to 20newsgroups/.
2. Run get_windows.ipynb to prepare data.
3. Run python train.py for training.
4. Run explore_trained_model.ipynb.

To use this on your data you need to edit get_windows.ipynb. Also there are hyperparameters in 20newsgroups/train.py, utils/training.py, utils/lda2vec_loss.py.

Implementation details

• I use vanilla LDA to initialize lda2vec (topic assignments for each document). It is not like in the original paper. It is not how it supposed to work. But without this results are quite bad.
  Also I use temperature to smoothen the initialization in the hope that lda2vec will have a chance to find better topic assignments.
• I add noise to some gradients while training.
• I reweight loss according to document lengths.
• Before training lda2vec I train 50-dimensional skip-gram word2vec to initialize the word embeddings.
• For text preprocessing:
  1. do word lemmatization
  2. remove rare and frequent words

Requirements

• pytorch 0.2, spacy 1.9, gensim 3.0
• numpy, sklearn, tqdm
• matplotlib, Multicore-TSNE