A comparative evaluation of off-the-shelf distributed semantic representations for modelling behavioural data.
In this paper we carry out an extensive comparison of many off-the-shelf distributed semantic vectors representations of words, for the purpose of making predictions about behavioural results or human annotations of data. In doing this comparison we also provide a guide for how vector similarity computations can be used to make such predictions, and introduce many resources available both in terms of datasets and of vector representations. Finally, we discuss the shortcomings of this approach and future research directions that might address them.
a Medical Imaging Technologies, Siemens Healthcare , USA.
Francisco
F
Pereira
b Department of Psychology and Center for Brain Science , Harvard University , Cambridge , USA.
Samuel
S
Gershman
c Princeton Neuroscience Institute , Princeton University , Princeton , USA.
Samuel
S
Ritter
d Google DeepMind , London , UK.
Matthew
M
Botvinick
D035841:Behavioral Research / Q000379:methods* / Q000706:statistics & numerical data; D003210:Concept Formation / Q000502:physiology*; D006801:Humans; D008962:Models, Theoretical; D012660:Semantics
Distributed semantic representation; evaluation; semantic space; semantic vector