Transfer learning and semi-supervised learning with ruimtehol

Last week the R package ruimtehol was updated on CRAN giving R users who perform Natural Language Processing access to the possibility to

  • Allow to do semi-supervised learning (learning where you have both text as labels but not always both of them on the same document identifier.
  • Allow to do transfer learning by passing on an embedding matrix (e.g. obtained via fasttext or Glove or the like) and keep on training based on that matrix or just use the embeddings in your Natural Language Processing flow.

More information can be found in the package vignette shown below or which you can obtain by installing the package and visiting the vignette with the following R code. Enjoy!

vignette("ground-control-to-ruimtehol", package = "ruimtehol")


Koning Filip lijkt op ...

Last call for the course on Text Mining with R, held next week in Leuven, Belgium on April 1-2. Viewing the course description as well as subscription can be done at

Some things you'll learn ... is that King Filip of Belgium is similar to public expenses if we just look at open data from questions and answers in Belgian parliament (retrieved from here Proof is below. See you next week.koning filip

data("dekamer", package = "ruimtehol")
dekamer$x <- strsplit(dekamer$question, "\\W")
dekamer$x <- lapply(dekamer$x, FUN = function(x) setdiff(x, ""))
dekamer$x <- sapply(dekamer$x, FUN = function(x) paste(x, collapse = " "))
dekamer$x <- tolower(dekamer$x)
dekamer$y <- strsplit(dekamer$question_theme, split = ",")
dekamer$y <- lapply(dekamer$y, FUN=function(x) gsub(" ", "-", x))
model <- embed_tagspace(x = dekamer$x, y = dekamer$y,
                        early_stopping = 0.8, validationPatience = 10,
                        dim = 50,
                        lr = 0.01, epoch = 40, loss = "softmax", adagrad = TRUE,
                        similarity = "cosine", negSearchLimit = 50,
                        ngrams = 2, minCount = 2)embedding_words  <- as.matrix(model, type = "words")
embedding_labels <- as.matrix(model, type = "labels", prefix = FALSE)
embedding_person <- starspace_embedding(model, tolower(c("Theo Francken")))
embedding_person <- starspace_embedding(model, tolower(c("Koning Filip")))
similarities <- embedding_similarity(embedding_person, embedding_words, top = 9)
similarities <- subset(similarities, !term2 %in% c("koning", "filip"))
similarities$term <- factor(similarities$term2, levels = rev(similarities$term2))
plt1 <- barchart(term ~ similarity | term1, data = similarities,
         scales = list(x = list(relation = "free"), y = list(relation = "free")),
         col = "darkgreen", xlab = "Similarity", main = "Koning Filip lijkt op ...")similarities <- embedding_similarity(embedding_person, embedding_labels, top = 7)
similarities$term <- factor(similarities$term2, levels = rev(similarities$term2))
plt2 <- barchart(term ~ similarity | term1, data = similarities,
         scales = list(x = list(relation = "free"), y = list(relation = "free")),
         col = "darkgreen", xlab = "Similarity", main = "Koning Filip lijkt op ...")
c(plt1, plt2)

Human Face Detection with R

Doing human face detection with computer vision is probably something you do once unless you work for police departments, you work in the surveillance industry or for the Chinese government. In order to reduce the time you lose on that small exercise, bnosac created a small R package (source code available at which wraps the weights of a Single Shot Detector (SSD) Convolutional Neural Network which was trained with the Caffe Deep Learning kit. That network allows to detect human faces in images. An example is shown below (tested on Windows and Linux).

install.packages("image.libfacedetection", repos = "")
image <- image_read("")
faces <- image_detect_faces(image)
plot(faces, image, border = "red", lwd = 7, col = "white")

libfacedetection example

What you get out of this is for each face the x/y locations and the width and height of the face. If you want to extract only the faces, loop over the detected faces and get them from the image as shown below.

allfaces <- Map(
    x      = faces$detections$x,
    y      = faces$detections$y,
    width  = faces$detections$width,
    height = faces$detections$height,
    f = function(x, y, width, height){
      image_crop(image, geometry_area(x = x, y = y, width = width, height = height))
allfaces <-, allfaces)

Hope this gains you some time when doing which seems like a t-test of computer vision. Want to learn more on computer vision, next time just follow our course on Computer Vision with R and Python:

Making thematic maps for Belgium

For people from Belgium working in R with spatial data, you can find excellent workshop material on creating thematic maps for Belgium at The workshop was given by Maarten Hermans from HIVA - Onderzoeksinstituut voor Arbeid en Samenleving.
The plots are heavily based on BelgiumMaps.Statbel - an R package from bnosac released 2 years ago (more info at
thematic maps r

An overview of the NLP ecosystem in R (#nlproc #textasdata)

At BNOSAC, R is used a lot to perform text analytics as it is an excellent tool that provides anything a data scientist needs to perform data analysis on text in a business settings. For users unfamiliar with all the possibilities that the wealth of R packages offers regarding text analytics, we've made this small mindmap showing a list of techniques and R packages that are used frequently in text mining projects set up by BNOSAC. Download the image and let your eyes zoom in on the different topics. Hope it broadens your idea of what is possible. Want to learn more or get hands on:

NLP R ecosystem