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I have a dataset of 2000 documents where avg doc size is 300 words. The vocab is dominated by domain-specific words.

My goal is to find similar documents. For this, I tried LDA, LSI, Doc2Vec (topics=100) but results are not great. LSI is better than the others in my dataset. I also tried word2vec (size=100) & word movers distance but again no luck. I am thinking of trying POS tags & then building some ontology model but not sure.

Are my results poor because of a bad dataset? What are some other techniques that I can try?

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  • $\begingroup$ Just a gut feeling from me, but 100 topics sounds like a lot with only 2000 docs. Where does 100 come from? Did you try fewer topics? $\endgroup$ – kbrose Nov 1 '17 at 18:18
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Similarity of documents can be done with varied approaches.

As your documents are based on domain based words, you could employ a tfidf representation for each document and compute similarity based on this.

Previously, I have used word2vec representations of words and constructed document vectors by taking an average of all the vectors ( as one of the approaches ) and did a cosine similarity between these vectors. Basically an n*n similarity computation.

When you do topic modelling, even though the idea is to find documents which have similar topic distribution, there is a prior to the approach i.e identifying number of topics. Just using randomly 100 topics without looking at the distribution of the words might lead you on a wrong path.

A very interesting approach I have recently come across is combining topic modelling and word vectors, here is the link to a blog by stitchfix : http://multithreaded.stitchfix.com/blog/2016/05/27/lda2vec/

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