Abstract
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.
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Notes
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The test data is available at: https://github.com/glushkovato/hashtag_segmentation.
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The paper was prepared within the framework of the HSE University Basic Research Program and funded by the Russian Academic Excellence Project ’5-100’.
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Glushkova, T., Artemova, E. (2023). Char-RNN and Active Learning for Hashtag Segmentation. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13451. Springer, Cham. https://doi.org/10.1007/978-3-031-24337-0_12
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