Timeline for Assign words to various topics
Current License: CC BY-SA 3.0
16 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 28, 2017 at 6:09 | answer | added | Prayalankar | timeline score: 1 | |
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| Jul 1, 2016 at 21:03 | comment | added | Emre | When you find a matrix factorized topic model, you need to add a term to the objective function to constrain or encourage the document vectors to have the desired property. There is usually a regularization term for sparsity or energy. LJB would simply need another regularization term. The document vector is the embedding of the document in the topic space. For more on language embeddings look up word2vec. | |
| Jul 1, 2016 at 20:58 | comment | added | Ryan Zotti | @Emre what do you mean by "add an appropriate regularization term to ensure related terms have similar embeddings"? I understand regularization and I also understand matrix factorization, but I don't understand how you're referring to them in this context. I'm also not sure what you mean by "embeddings" either. | |
| Jul 1, 2016 at 20:54 | answer | added | Ryan Zotti | timeline score: 0 | |
| Jul 1, 2016 at 7:42 | comment | added | Emre | It's just a matter of adding an appropriate regularization term to ensure related terms have similar embeddings. It's especially easy if you use the matrix factorization model. In any case, you'll have to write some code, but it should not be that hard. Welcome to DataScience.SE! | |
| Jul 1, 2016 at 3:14 | history | asked | LJB | CC BY-SA 3.0 |