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Word embeddings have been extensively studied in large text datasets.However, only a few studies analyze semantic representations of small corpora,particularly relevant in single-person text production studies. In the presentpaper, we compare Skip-gram and LSA capabilities in this scenario, and we testboth techniques to extract relevant semantic patterns in single-series dreamsreports. LSA showed better performance than Skip-gram in small size trainingcorpus in two semantic tests. As a study case, we show that LSA can capturerelevant words associations in dream reports series, even in cases of smallnumber of dreams or low-frequency words. We propose that LSA can be used toexplore words associations in dreams reports, which could bring new insightinto this classic research area of psychology

Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database