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I will cite the FAQ from . First for perplexity: How should I set the perplexity in t-SNE? The performance of t-SNE is fairly robust under different settings of the perplexity. The most appropriate value depends on the density of your data. Loosely speaking one could say that a larger / denser dataset requires a larger perplexity. Typical values for the perplexity range between 5 and 50. For ...

 

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Thus higher the log-likelihood lower the perplexity. Question: Doesn't increasing log-likelihood indicate over-fitting? Criteria like AIC and BIC are specifically designed to take into account likelihood and penalize for number of parameters in the model to avoid over-fitting. Why is lower perplexity an indicator of better generalization ...

 

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It's impossible to say. Increasing n trades off variance in exchange for less bias. The only way to know whether increasing n reduces perplexity is by already knowing how exactly how the text was generated. In practice unigram models tend to underfit on non-trivial text datasets. 10-gram models trained on small datasets tend to overfit. It's difficult and not really useful to hypothesize ...

 

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If I understand it correctly this means that I could calculate the perplexity of a single sentence. What does it mean if I'm asked to calculate the perplexity on a whole corpus?

 

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I came across term perplexity which refers to the log-averaged inverse probability on unseen data. Wikipedia article on perplexity does not give an intuitive meaning for the same. This perplexity