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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 present paper, we compare Skip-gram and LSA capabilities in this scenario, and we test both techniques to extract relevant semantic patterns in single-series dreams reports. LSA showed better performance than Skip-gram in small size training corpus in two semantic tests. As adoi:10.1016/j.concog.2017.09.004 pmid:28943127 fatcat:d6pfu3muwzfo5g6cj66t5z3ic4