http://blog.csdn.net/pipisorry/article/details/45307369
Although LDA is a great algorithm for topic-modelling, it still has some limitations, mainly due to the fact that it’s has become popular and available to the mass recently.
One major limitation is perhaps given by its underlying unigram text model: LDA doesn’t consider themutual position of the words in the document. Documents like “Man, I love this can” and “I can love this man” are probably modelled the same way. It’s also true that for longer documents, mismatching topics is harder. To overcome this limitation, at the cost of almost square the complexity, you can use 2-grams (or N-grams)along with 1-gram.
Another weakness of LDA is in the topics composition: they’re overlapping. In fact, you can find thesame word in multiple topics(the example above, of the word “can”, is obvious). The generated topics, therefore, are not independent andorthogonal(正交的) like in a PCA-decomposed basis, for example. This implies that you must pay lots of attention while dealing with them (e.g. don’t usecosine similarity).
For a more structured approach - especially if the topic composition is very misleading - you might consider thehierarchical variation of LDA, named H-LDA, (or simply Hierarchical LDA). In H-LDA, topics are joined together in a hierarchy by using a Nested Chinese Restaurant Process (NCRP). This model is more complex than LDA, and the description is beyond the goal of this blog entry, but if you like to have an idea of the possible output, here it is. Don’t forget that we’re still in theprobabilistic world: each node of the H-DLA tree is a topic distribution.
[http://engineering.intenthq.com/2015/02/automatic-topic-modelling-with-lda/]
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原文：http://blog.csdn.net/pipisorry/article/details/45307369
