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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)
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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
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