Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database
Abstract
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
Cite
@article{arxiv.1610.01520,
title = {Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database},
author = {Edgar Altszyler and Mariano Sigman and Sidarta Ribeiro and Diego Fernández Slezak},
journal= {arXiv preprint arXiv:1610.01520},
year = {2018}
}