[Stevens12] performed an extensive study of the these two measures on one dataset, New York Times articles (92,600 New York Times articles from 2003 and a vocabulary size of 35,836 tokens after removing the ones occurring less than 200 times throughout the corpus), in order to compare the three models: LDA, LSA with SVD, and LSA with NMF.

However, they did not use the formulations of the measures used by their original authors.

### UMass measure

For the UMass measure, they introduced a free parameter $\epsilon$, instead of just one, for smoothing in the pairwise scoring function
$$
\text{score}_{\text{UMass}}(w_i, w_j) =
\log
\frac{D(w_i, w_j) + \epsilon}{D(w_i)}
$$
and tried both $\epsilon = 1$ and $\epsilon = 10^{-12}$. Setting $\epsilon = 10^{-12}$ seems to over-penalize pairs that never occur together, i.e. when $D(w_i, w_j) = 0$, as it will decrease the score of that pair by 12
$$
\text{score}_{\text{UMass}}(w_i, w_j)
= \log \frac{\epsilon}{D(w_i)}
= -12 - \log D(w_i)
$$
which is very large in the log space of document counts. It is also equivalent to say that we would have to see $10^{12}$ more documents to see the two words appearing only once together. Having $\epsilon = 1$ looks more reasonable as it is treating pairs that appear once throughout the corpus, i.e. when $D(w_i, w_j) = 1$, and pairs that never appear, i.e. when $D(w_i, w_j) = 0$, roughly in the same order of magnitude.

### UCI measure

For the UCI measure, they introduced a smoothing $\epsilon$ in the pairwise score that the original authors did not
$$ \text{score}_{\text{UCI}}(w_i, w_j) = \log \frac{p(w_i, w_j) + \epsilon}{p(w_i)p(w_j)}$$
with $\epsilon$ initially set to one. Here, we are at a different scale as we are dealing with probabilities and not counts. As $\epsilon = 1$ is likely to be huge compared to $p(w_i, w_j)$, the smoothing parameter artificially increases the topic coherence, and not even by the same amount for all pairs of words. Then, using $\epsilon = 10^{-12}$, which is likely to be smaller than $p(w_i, w_j)$, caused big changes, and therefore the authors concluded that coherence measures depend heavily on smoothing $\epsilon$.

Nonetheless a smoothing parameter is required to avoid taking the logarithm of zero. A reasonable choice for smoothing is to assume that every pair of words is present at least once in the corpus, and compute the empirical probability
$$ p(w_i, w_j) = \frac{D_{\text{Wikipedia}}(w_i, w_j) + 1}{D_{\text{Wikipedia}}}$$
which is the same smoothing as the UMass measure.