We're undergoing an internal software audit and identified at least one textract component released under the Affero GPL: the EbookLib.

Lawyers are getting a bit antsy over this. In general, compatibility with GPL means that code released under a different license (e.g. MIT) and combined with GPL'd code must be released under GPL. This might create a b

Hi there,

I think there might be a mistake in the documentation. The Understanding Scaled F-Score section says

The F-Score of these two values is defined as:

$$ \mathcal{F}_\beta(\mbox{prec}, \mbox{freq}) = (1 + \beta^2) \frac{\mbox{prec} \cdot \mbox{freq}}{\beta^2 \cdot \mbox{prec} + \mbox{freq}}. $$

$\beta \in \mathcal{R}^+$ is a scaling factor where frequency is favored if $\beta

In the current tidytext document explaining about the tidy approach to stm object, there is no specific example of how to add covariates.

I wanted to try that out with stm::gadarian data using prevalence = ~treatment + s(pid_rep) covariate formula; however, I have fac

Hello,

I am getting the following error message "error: package directory 'rake_nltk' does not exist" when installing rake-nltk with:
git clone https://github.com/csurfer/rake-nltk.git
python rake-nltk/setup.py install :

I also tried the option pip install rake-nltk but the installation also fails:

File "/tmp/pip-build-2zTHYP/rake-nltk/setup.py", line 17, in _post_install
import

I would like to know what all the abbreviations mean? Some I can guess, like "PUNCT", but no idea what "X" might be. I want to retain contractions, but hard to choose options without documentation.

Thanks. Great performance code!

When using artm.SmoothSparseThetaRegularizer(tau=tau_val) with tau_val<0 we get some \Theta matrix columns filled totally with zeros. From perplexity score, the optimization converges. The quantity of documents with all zeros in their \Theta columns grows as $tau_val->-\infty$.
How it's possible that optimization constraint on theta columns violates?

Hi,
I used to have a previous version of LDAvis (2014) installed with devtools.
In the version I had of LDAvis I would call createJSON as:
json <- createJSON(K, phi, term.frequency, vocab, topic.proportions)

Today I updated my R packages and have a newer vesion of LDAvis (from CRAN) which uses createJSON as:
json <- createJSON(phi, theta, doc.length, vocab, term.frequency)

I'm using MALLET for t

I think it is necessary to add an experiment that compare the test accuracy of the original text and the adversarial text examples in the target model to judge whether the adversarial text examples really reduce the accuracy.

is someone familiar with the Ontology process and can share an RDF file for example?
Super Thanks :)