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        <title>SiPOS: A Benchmark Dataset for Sindhi Part-of-Speech Tagging</title>
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        <namePart type="given">Wazir</namePart>
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        <namePart type="given">Zenglin</namePart>
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            <title>Proceedings of the Student Research Workshop Associated with RANLP 2021</title>
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            <namePart type="given">Souhila</namePart>
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    <abstract>In this paper, we introduce the SiPOS dataset for part-of-speech tagging in the low-resource Sindhi language with quality baselines. The dataset consists of more than 293K tokens annotated with sixteen universal part-of-speech categories. Two experienced native annotators annotated the SiPOS using the Doccano text annotation tool with an inter-annotation agreement of 0.872. We exploit the conditional random field, the popular bidirectional long-short-term memory neural model, and self-attention mechanism with various settings to evaluate the proposed dataset. Besides pre-trained GloVe and fastText representation, the character-level representations are incorporated to extract character-level information using the bidirectional long-short-term memory encoder. The high accuracy of 96.25% is achieved with the task-specific joint word-level and character-level representations. The SiPOS dataset is likely to be a significant resource for the low-resource Sindhi language.</abstract>
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%0 Conference Proceedings
%T SiPOS: A Benchmark Dataset for Sindhi Part-of-Speech Tagging
%A Ali, Wazir
%A Xu, Zenglin
%A Kumar, Jay
%Y Djabri, Souhila
%Y Gimadi, Dinara
%Y Mihaylova, Tsvetomila
%Y Nikolova-Koleva, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2021
%D 2021
%8 September
%I INCOMA Ltd.
%C Online
%F ali-etal-2021-sipos-benchmark
%X In this paper, we introduce the SiPOS dataset for part-of-speech tagging in the low-resource Sindhi language with quality baselines. The dataset consists of more than 293K tokens annotated with sixteen universal part-of-speech categories. Two experienced native annotators annotated the SiPOS using the Doccano text annotation tool with an inter-annotation agreement of 0.872. We exploit the conditional random field, the popular bidirectional long-short-term memory neural model, and self-attention mechanism with various settings to evaluate the proposed dataset. Besides pre-trained GloVe and fastText representation, the character-level representations are incorporated to extract character-level information using the bidirectional long-short-term memory encoder. The high accuracy of 96.25% is achieved with the task-specific joint word-level and character-level representations. The SiPOS dataset is likely to be a significant resource for the low-resource Sindhi language.
%U https://aclanthology.org/2021.ranlp-srw.4
%P 22-30

Markdown (Informal)

[SiPOS: A Benchmark Dataset for Sindhi Part-of-Speech Tagging](https://aclanthology.org/2021.ranlp-srw.4) (Ali et al., RANLP 2021)

• SiPOS: A Benchmark Dataset for Sindhi Part-of-Speech Tagging (Ali et al., RANLP 2021)

ACL

• Wazir Ali, Zenglin Xu, and Jay Kumar. 2021. SiPOS: A Benchmark Dataset for Sindhi Part-of-Speech Tagging. In Proceedings of the Student Research Workshop Associated with RANLP 2021, pages 22–30, Online. INCOMA Ltd..