RT Journal Article
T1 Bridging the theoretical gap between semantic representation models without the pressure of a ranking: Some lessons learnt from LSA
A1 Jorge Botana, Guillermo de
A1 Olmos, Ricardo
A1 Luzón Encabo, José María
AB In recent years, latent semantic analysis (LSA) has reached a level of maturity at which its presence is ubiquitous in technology as well as in simulation of cognitive processes. In spite of this, in recent years there has been a trend of subjecting LSA to some criticisms, usually because it is compared to other models in very specific tasks and conditions and sometimes without having good knowledge of what the semantic representation of LSA means, and without exploiting all the possibilities of which LSA is capable other than the cosine. This paper provides a critical review to clarify some of the misunderstandings regarding LSA and other space models. The historical stability of the predecessors of LSA, the representational structure of word meaning and the multiple topologies that could arise from a semantic space, the computation of similarity, the myth that LSA dimensions have no meaning, the computational and algorithm plausibility to account for meaning acquisition in LSA (in contrast to others models based on online mechanisms), the possibilities of spatial models to substantiate recent proposals, and, in general, the characteristics of classic vector models and their ease and flexibility to simulate some cognitive phenomena will be reviewed. The review highlights the similarity between LSA and other techniques and proposes using long LSA experiences in other models, especially in predicting models such as word2vec. In sum, it emphasizes the lessons that can be learned from comparing LSA-based models to other models, rather than making statements about “the best.”
PB Springer
YR 2020
FD 2020-07-01
LK https://hdl.handle.net/20.500.14352/127360
UL https://hdl.handle.net/20.500.14352/127360
LA eng
NO Jorge-Botana, G., Olmos, R. & Luzón, J.M. Bridging the theoretical gap between semantic representation models without the pressure of a ranking: some lessons learnt from LSA. Cogn Process 21, 1–21 (2020). https://doi.org/10.1007/s10339-019-00934-x
DS Docta Complutense
RD 30 nov 2025