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i.e. as a weighted geometric average of the inverses of the probabilities. For a continuous distribution, the sum would turn into a integral. The article also gives a way of estimating perplexity for a model using N pieces of test data. 2 − ∑N i = 11 Nlog2q (xi) which could also be written.
9. Why does larger perplexity tend to produce clearer clusters in t-SNE? By reading the original paper, I learned that the perplexity in t-SNE is 2 2 to the power of Shannon entropy of the conditional distribution induced by a data point. And it is mentioned in the paper that it can be interpreted as a smooth measure of the effective number of ...
7. While reading Laurens van der Maaten's paper about t-SNE we can encounter the following statement about perplexity: The perplexity can be interpreted as a smooth measure of the effective number of neighbors. The performance of SNE is fairly robust to changes in the perplexity, and typical values are between 5 and 50.
The perplexity, used by convention in language modeling, is monotonically decreasing in the likelihood of the test data, and is algebraicly equivalent to the inverse of the geometric mean per-word likelihood. A lower perplexity score indicates better generalization performance. I.e, a lower perplexity indicates that the data are more likely.
I trained 35 LDA models with different values for k, the number of topics, ranging from 1 to 100, using the train subset of the data. Afterwards, I estimated the per-word perplexity of the models using gensim's multicore LDA log_perplexity function, using the test held-out corpus:: Eventually, keeping in mind that true k is 20 for the used ...
Because the avg_probs are less random (think less diffused), it would have a lower entropy and thus lower perplexity. We want a high perplexity because that means that more of the indices in our codebook were used. When more indices are used, we get more perplexity due to more uniformity in avg_probs.
Here, "low" perplexity is a relative notion; there's no absolute scale of good<-->bad perplexity. $\endgroup$
To evaluate the effectiveness of their model, they use perplexity, defined as exp(− 1 NΣNn 1 Lnlog p(Xn)) e x p (− 1 N Σ n N 1 L n log p (X n)). Where N N is number of documents, Ln L n is number of words in document n n, and p(Xn) p (X n) is the probability of observing document Xn X n (calculated by taking the product of the per-word ...
Now, I am tasked with trying to find the perplexity of the test data (the sentences for which I am predicting the language) against each language model. I have read the relevant section in "Speech and Language Processing" by Jurafsky and Martin , as well as scoured the internet to try to figure out what it means to take the perplexity in the ...
Result is 52.64296. I interpreted the probabilities here as: Let's imagine there are 120000 words in total, where by probability distribution: Operator, Sales and Technical Support each occur 30,000 times (P = 1/4) Each of the name occur only once (P = 1/120000). Perplexity can be calculated then, using the formula: (1 430000 ∗ 1 430000 ∗ 1 ...