Multi-task Pairwise Neural Ranking for Hashtag Segmentation

Mounica Maddela, Wei Xu, Daniel Preoţiuc-Pietro
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
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
more » ... of approaches for hashtag segmentation by framing it as a pairwise ranking problem between candidate segmentations. 1 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 Se-mEval 2017 sentiment analysis dataset.
doi:10.18653/v1/p19-1242 dblp:conf/acl/MaddelaXP19 fatcat:u64clehp4jbjbnytkb47z2oxqa