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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 Joint learning of morphology and syntax with cross-level contextual information flow Can Buglalilar, Burcu; Aleçakır, Hüseyin; Manandhar, Suresh... In: 2022 ; 1 ; 33 (2022) BASE Show details 2 A GRU-based pipeline approach for word-sentence segmentation and punctuation restoration in English Sivakumar, Jasivan; Muga, Jake; Spadavecchia, Flavio; White, Daniel; Can Buglalilar, Burcu. - : IEEE, 2022 Abstract: This is an accepted manuscript of an article published by IEEE in Proceedings of 2021 International Conference on Asian Language Processing (IALP) on 20 Jan 2022. Available online at https://doi.org/10.1109/IALP54817.2021.9675269 The accepted version of the publication may differ from the final published version. ; In this study, we propose a Gated Recurrent Unit (GRU) model to restore the following features: word and sentence boundaries, periods, commas, and capitalisation for unformatted English text. We approach feature restoration as a binary classification task where the model learns to predict whether a feature should be restored or not. A pipeline approach is proposed, in which only one feature (word boundary, sentence boundary, punctuation, capitalisation) is restored in each component of the pipeline model. To optimise the model, we conducted a grid search on the parameters. The effect of changing the order of the pipeline is also investigated experimentally; PERIODS > COMMAS > SPACES > CASING yielded the best result. Our findings highlight several specifcaction points with optimisation potential to be targeted in follow-up research. Keyword: deep learning; graph neural networks; sematics URL: http://hdl.handle.net/2436/624438 https://doi.org/10.1109/IALP54817.2021.9675269 BASE Hide details

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