Unsupervised automatic tagging algorithms?
These guys propose an alternative to LDA.
Automatic Tag Recommendation Algorithms for Social Recommender Systems http://research.microsoft.com/pubs/79896/tagging.pdf
Haven't read thru the whole paper but they have two algorithms:
- Supervised learning version. This isn't that bad. You can use Wikipedia to train the algorithm
- "Prototype" version. Haven't had a chance to go thru this but this is what they recommend
UPDATE: I've researched this some more and I've found another approach. Basically, it's a two-stage approach that's very simple to understand and implement. While too slow for 100,000s of documents, it (probably) has good performance for 1000s of docs (so it's perfect for tagging a single user's documents). I'm going to try this approach and will report back on performance/usability.
In the mean time, here's the approach:
- Use TextRank as per http://qr.ae/36RAP to generate a tag list for a single document. This generates a tag list for a single document independent of other documents.
- Use the algorithm from "Using Machine Learning to Support Continuous Ontology Development" (https://www.researchgate.net/publication/221630712_Using_Machine_Learning_to_Support_Continuous_Ontology_Development) to integrate the tag list (from step 1) into the existing tag list.
The most common unsupervised machine learning model for this type of task is Latent Dirichlet Allocation (LDA). This model automatically infers a collection of topics over a corpus of documents based on the words in those documents. Running LDA on your set of documents would assign words with probability to certain topics when you search for them, and then you could retrieve the documents with the highest probabilities to be relevant to that word.
There have been some extensions to images and music as well, see http://cseweb.ucsd.edu/~dhu/docs/research_exam09.pdf.
LDA has several efficient implementations in several languages:
- many implementations from the original researchers
- http://mallet.cs.umass.edu/, written in Java and recommended by others on SO
- PLDA: a fast, parallelized C++ implementation