We explore the abilities of character recurrent neural network (char-RNN) for hashtag segmentation. Our approach to the task is the following: we generate synthetic training dataset according to frequent n-grams that satisfy predefined morpho-syntactic patterns to avoid any manual annotation. The active learning strategy limits the training dataset and selects informative training subset. The approach does not require any language-specific settings and is compared for two languages, which differ in inflection degree.
@article{arxiv.1911.03270,
title = {Char-RNN and Active Learning for Hashtag Segmentation},
author = {Taisiya Glushkova and Ekaterina Artemova},
journal= {arXiv preprint arXiv:1911.03270},
year = {2023}
}