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        <title>Empirical Evaluation of Character-Based Model on Neural Named-Entity Recognition in Indonesian Conversational Texts</title>
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    <name type="personal">
        <namePart type="given">Kemal</namePart>
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        <namePart type="given">Samuel</namePart>
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            <title>Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text</title>
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        <name type="personal">
            <namePart type="given">Wei</namePart>
            <namePart type="family">Xu</namePart>
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            <namePart type="given">Alan</namePart>
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            <namePart type="given">Tim</namePart>
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    <abstract>Despite the long history of named-entity recognition (NER) task in the natural language processing community, previous work rarely studied the task on conversational texts. Such texts are challenging because they contain a lot of word variations which increase the number of out-of-vocabulary (OOV) words. The high number of OOV words poses a difficulty for word-based neural models. Meanwhile, there is plenty of evidence to the effectiveness of character-based neural models in mitigating this OOV problem. We report an empirical evaluation of neural sequence labeling models with character embedding to tackle NER task in Indonesian conversational texts. Our experiments show that (1) character models outperform word embedding-only models by up to 4 F1 points, (2) character models perform better in OOV cases with an improvement of as high as 15 F1 points, and (3) character models are robust against a very high OOV rate.</abstract>
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    <identifier type="doi">10.18653/v1/W18-6112</identifier>
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%0 Conference Proceedings
%T Empirical Evaluation of Character-Based Model on Neural Named-Entity Recognition in Indonesian Conversational Texts
%A Kurniawan, Kemal
%A Louvan, Samuel
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kurniawan-louvan-2018-empirical
%X Despite the long history of named-entity recognition (NER) task in the natural language processing community, previous work rarely studied the task on conversational texts. Such texts are challenging because they contain a lot of word variations which increase the number of out-of-vocabulary (OOV) words. The high number of OOV words poses a difficulty for word-based neural models. Meanwhile, there is plenty of evidence to the effectiveness of character-based neural models in mitigating this OOV problem. We report an empirical evaluation of neural sequence labeling models with character embedding to tackle NER task in Indonesian conversational texts. Our experiments show that (1) character models outperform word embedding-only models by up to 4 F1 points, (2) character models perform better in OOV cases with an improvement of as high as 15 F1 points, and (3) character models are robust against a very high OOV rate.
%R 10.18653/v1/W18-6112
%U https://aclanthology.org/W18-6112
%U https://doi.org/10.18653/v1/W18-6112
%P 85-92

Markdown (Informal)

[Empirical Evaluation of Character-Based Model on Neural Named-Entity Recognition in Indonesian Conversational Texts](https://aclanthology.org/W18-6112) (Kurniawan & Louvan, WNUT 2018)

• Empirical Evaluation of Character-Based Model on Neural Named-Entity Recognition in Indonesian Conversational Texts (Kurniawan & Louvan, WNUT 2018)

ACL

• Kemal Kurniawan and Samuel Louvan. 2018. Empirical Evaluation of Character-Based Model on Neural Named-Entity Recognition in Indonesian Conversational Texts. In Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, pages 85–92, Brussels, Belgium. Association for Computational Linguistics.