Abstract
Punctuation restoration is the process of adding punctuation symbols to raw text. It is typically used as a post-processing task of Automatic Speech Recognition (ASR) systems. In this paper we present an approach for punctuation restoration for texts in Slovene language. The system is trained using bi-directional Recurrent Neural Networks fed by word embeddings only. The evaluation results show our approach is capable of restoring punctuations with a high recall and precision. The F1 score is specifically high for commas and periods, which are considered most important punctuation symbols for the understanding of the ASR based transcripts.
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Bajec, M., Janković, M., Žitnik, S., Bajec, I.L. (2020). Punctuation Restoration System for Slovene Language. In: Dalpiaz, F., Zdravkovic, J., Loucopoulos, P. (eds) Research Challenges in Information Science. RCIS 2020. Lecture Notes in Business Information Processing, vol 385. Springer, Cham. https://doi.org/10.1007/978-3-030-50316-1_31
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DOI: https://doi.org/10.1007/978-3-030-50316-1_31
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