Comparative study of LSA vs Word2vec embeddings in small corpora: a case   study in dreams database
  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 a study case, we show that LSA can capture relevant words associations in dream reports series, even in cases of small number of dreams or low-frequency words. We propose that LSA can be used to explore words associations in dreams reports, which could bring new insight into this classic research area of psychology 
Edgar Altszyler, Mariano Sigman, Sidarta Ribeiro and Diego Fern\'andez   Slezak
cs.CL cs.IR