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        <title>Multi-task Pairwise Neural Ranking for Hashtag Segmentation</title>
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        <namePart type="given">Mounica</namePart>
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    <abstract>Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics. However, the semantic content of hashtags is not straightforward to infer as these represent ad-hoc conventions which frequently include multiple words joined together and can include abbreviations and unorthodox spellings. We build a dataset of 12,594 hashtags split into individual segments and propose a set of approaches for hashtag segmentation by framing it as a pairwise ranking problem between candidate segmentations. Our novel neural approaches demonstrate 24.6% error reduction in hashtag segmentation accuracy compared to the current state-of-the-art method. Finally, we demonstrate that a deeper understanding of hashtag semantics obtained through segmentation is useful for downstream applications such as sentiment analysis, for which we achieved a 2.6% increase in average recall on the SemEval 2017 sentiment analysis dataset.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1242</identifier>
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%0 Conference Proceedings
%T Multi-task Pairwise Neural Ranking for Hashtag Segmentation
%A Maddela, Mounica
%A Xu, Wei
%A Preoţiuc-Pietro, Daniel
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F maddela-etal-2019-multi
%X Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics. However, the semantic content of hashtags is not straightforward to infer as these represent ad-hoc conventions which frequently include multiple words joined together and can include abbreviations and unorthodox spellings. We build a dataset of 12,594 hashtags split into individual segments and propose a set of approaches for hashtag segmentation by framing it as a pairwise ranking problem between candidate segmentations. Our novel neural approaches demonstrate 24.6% error reduction in hashtag segmentation accuracy compared to the current state-of-the-art method. Finally, we demonstrate that a deeper understanding of hashtag semantics obtained through segmentation is useful for downstream applications such as sentiment analysis, for which we achieved a 2.6% increase in average recall on the SemEval 2017 sentiment analysis dataset.
%R 10.18653/v1/P19-1242
%U https://aclanthology.org/P19-1242
%U https://doi.org/10.18653/v1/P19-1242
%P 2538-2549

Markdown (Informal)

[Multi-task Pairwise Neural Ranking for Hashtag Segmentation](https://aclanthology.org/P19-1242) (Maddela et al., ACL 2019)

• Multi-task Pairwise Neural Ranking for Hashtag Segmentation (Maddela et al., ACL 2019)

ACL

• Mounica Maddela, Wei Xu, and Daniel Preoţiuc-Pietro. 2019. Multi-task Pairwise Neural Ranking for Hashtag Segmentation. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2538–2549, Florence, Italy. Association for Computational Linguistics.