Neural Text Modelling with R package ruimtehol
Last week the R package ruimtehol was released on CRAN (https://github.com/bnosac/ruimtehol) allowing R users to easily build and apply neural embedding models on text data.
It wraps the 'StarSpace' library https://github.com/facebookresearch/StarSpace allowing users to calculate word, sentence, article, document, webpage, link and entity 'embeddings'. By using the 'embeddings', you can perform text based multi-label classification, find similarities between texts and categories, do collaborative-filtering based recommendation as well as content-based recommendation, find out relations between entities, calculate graph 'embeddings' as well as perform semi-supervised learning and multi-task learning on plain text. The techniques are explained in detail in the paper: 'StarSpace: Embed All The Things!' by Wu et al. (2017), available at https://arxiv.org/abs/1709.03856.
You can get started with some common text analytical use cases by using the presentation we have built below. Enjoy!
Upcoming training schedule
Note also that you might be interested in the following courses held in Belgium
- 21-22/02/2018: Advanced R programming. Leuven (Belgium). Subscribe here
- 13-14/03/2018: Computer Vision with R and Python. Leuven (Belgium). Subscribe here
- 15/03/2019: Image Recognition with R and Python: Subscribe here
- 01-02/04/2019: Text Mining with R. Leuven (Belgium). Subscribe here