  Language identification is an important Natural Language Processing task. It
has been thoroughly researched in the literature. However, some issues are
still open. This work addresses the identification of the related low-resource
languages on the example of the Celtic language family.
  This work's main goals were: (1) to collect the dataset of three Celtic
languages; (2) to prepare a method to identify the languages from the Celtic
family, i.e. to train a successful classification model; (3) to evaluate the
influence of different feature extraction methods, and explore the
applicability of the unsupervised models as a feature extraction technique; (4)
to experiment with the unsupervised feature extraction on a reduced annotated
set.
  We collected a new dataset including Irish, Scottish, Welsh and English
records. We tested supervised models such as SVM and neural networks with
traditional statistical features alongside the output of clustering,
autoencoder, and topic modelling methods. The analysis showed that the
unsupervised features could serve as a valuable extension to the n-gram feature
vectors. It led to an improvement in performance for more entangled classes.
The best model achieved a 98\% F1 score and 97\% MCC. The dense neural network
consistently outperformed the SVM model.
  The low-resource languages are also challenging due to the scarcity of
available annotated training data. This work evaluated the performance of the
classifiers using the unsupervised feature extraction on the reduced labelled
dataset to handle this issue. The results uncovered that the unsupervised
feature vectors are more robust to the labelled set reduction. Therefore, they
proved to help achieve comparable classification performance with much less
labelled data.
