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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 setdoi:10.18653/v1/p19-1242 dblp:conf/acl/MaddelaXP19 fatcat:u64clehp4jbjbnytkb47z2oxqa