A major update of spaCy (v2.1) was released recently. spaCy is one of the best and fastest tools for tokenization, part-of-speech tagging, dependency parsing, and entity recognition. In this post, I will discuss how it works with our spacyr package along with some tips on having multiple versions of spaCy using conda environments.
Good news: It works Our package spacyr is an R wrapper to the spaCy Python library. To work with the spacyr package, users have to prepare a Python environment with spaCy installed.
Introduction A frequent problem in processing texts is the need to segment one or few documents into many documents, based on segments that they contain marking units that the analyst will want to consider seperately.
This is a frequent feature of interview or debate transcripts, for instance, where a single long source document might contain numerous speech acts from individual speakers. For analysis, it’s likely that we would want to consider these speakers separately, perhaps with the ability to combine their speech acts later by spearker.
Quanteda Blog This is a placeholder for a brandnew quanteda blog.
We are so delighted to announce our new blog.
What is this blog for? This blog serves for several purposes:
Announcing new update on our quanteda-verse packages and services Sharing the information and tips on natural language processing especially in R We need contributors Here is a guideline for contributors.
What to contribute As we stated in the announcement, we would like to make this blog a forum for the people who are interested in natural language processing (NLP) from the newbies to the guru, and to disseminate the information about the latest updates in NLP world and also how-to or how-not-to analyze text as data.
To serve this goal, we would like to ask those who have a say on NLP to get involved in our blog as contributors of blog post.