SpaCy

Also look at stackoverflow.com/questions/56446478/spacy-en-model-issue/… - I just answered a similar question. ... For a Linux system run the below code in terminal if you would be using a virtual environment else skip first and second command : python -m venv .env source .env/bin/activate ... More on stackoverflow.com

I agree with both of the previous commenters in that: I use {spacyr} + {reticulate} for NLP work that involves using spaCy. This is because I like support for Python from RStudio IDE and rmarkdown. It's miles better for R + Python work than its closest neighbor, Jupyter Notebook. But at the same time, even as someone who also mostly uses R, I agree with Python's approach to NLP. Actually, if I'm being more precise, it's not really R vs. Python but more functional vs. object-oriented approaches to NLP. The difference is between working with words as observations representated as rows in dataframes versus working with words as objects that have special attributes. Let me extrapolate a bit more on my point #2. Consider a case where you want to know whether the head of a word is a Verb. In {spacyr}, this means finding the value of the head column at the row of the word, then finding the row of the head word using that value, then finding the value of the POS column at that row. This approach involves a lot of slice() and filter() and subsetting. In Python's spaCy, on the other hand, this just involves getting the attribute head of the word and then getting the attribute of that word. If that word is stored in a variable called my_word, checking whether the head of that word is a Verb is simply my_word.head.pos_ == "VERB" So at least in the case of analyzing depedency relations, the difference is between working with indices of a data frame of words vs. working with pointers to words that are accessible as an attribute of word objects. I favor the latter object-oriented approach in Python because it's faster, less code, more readable, and more intuitive. But that's probably partly because my work involves a lot of analyzing dependency relatios. tl;dr #1: Depends on the task, but object-oriented approach of Python is overall better IMO. But it's not an either-or: you can do all that work within the comfort of the RStudio IDE and your R-centered workflow with {reticulate} and R markdown. Just to be clear though, functional vs. object-oriented approach to NLP is also not really a black-white issue, much like "R vs. Python". In fact, the part that I love the most about the object-oriented NLP workflow in Python is list comprehension and that's about as functional programming as you can get. Working with a list of word objects is IMO the perfect marriage between functional and object-oriented programming as it vastly improves the quality of my work. tl;dr #2: If you're going to learn spaCy (the Python way), learn Python's list comprehension too. More on reddit.com

Hey, I tried to install spacy in python. It worked out and I can find it in the pip list in my terminal. But now if i try to run my python programme there is an error that says “no module named spacy”. I don’t know what to do because it is installed. Have you any idea? More on discuss.python.org

Both serve their own purpose I guess, but also consider Stanza More on reddit.com