#TagSpace: Semantic Embeddings from Hashtags

Abstract

We describe a convolutional neural net-work that learns feature representations for short textual posts using hashtags as a su-pervised signal. The proposed approach is trained on up to 5.5 billion words predict-ing 100,000 possible hashtags. As well as strong performance on the hashtag predic-tion task itself, we show that its learned representation of text (ignoring the hash-tag labels) is useful for other tasks as well. To that end, we present results on a docu-ment recommendation task, where it also outperforms a number of baselines. 1