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Short text messages such as tweets are very noisy and sparse in their use ofvocabulary. Traditional textual representations, such as tf-idf, havedifficulty grasping the semantic meaning of such texts, which is important inapplications such as event detection, opinion mining, news recommendation, etc.We constructed a method based on semantic word embeddings and frequencyinformation to arrive at low-dimensional representations for short textsdesigned to capture semantic similarity. For this purpose we designed aweight-based model and a learning procedure based on a novel median-based lossfunction. This paper discusses the details of our model and the optimizationmethods, together with the experimental results on both Wikipedia and Twitterdata. We find that our method outperforms the baseline approaches in theexperiments, and that it generalizes well on different word embeddings withoutretraining. Our method is therefore capable of retaining most of the semanticinformation in the text, and is applicable out-of-the-box.

Representation learning for very short texts using weighted word embedding aggregation