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We find that Skip-gram, GloVe and FastText embeddings tend to produce higher similarity between high-frequency words than between other frequency combinations. Query suggestion is a standard feature of screen-based search experiences, allowing users to explore additional topics. While performance of many text classification tasks has been recently improved due to Pretrained Language Models PLMs , in this paper we show that they still suffer from a performance gap when the underlying distribution of topics changes. Itaque lex pedagogus noster fuit in Christo ut ex fide iustificemur. Despite imposing a stronger independence assumption than the low-rank approach, we find that this formalism scales more effectively both as a language model and as an unsupervised parser.

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