Word embeddings are increasingly being used
as a tool to study word associations in specific
corpora. However, it is unclear whether such
embeddings reflect enduring properties of language
or if they are sensitive to inconsequential
variations in the source documents. We find
that nearest-neighbor distances are highly sensitive
to small changes in the training corpus
for a variety of algorithms. For all methods,
including specific documents in the training
set can result in substantial variations. We
show that these effects are more prominent for
smaller training corpora. We recommend that
users never rely on single embedding models
for distance calculations, but rather average
over multiple bootstrap samples, especially for
small corpora.
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How do blind people know that blue is cold? Distributional semantics encode color-adjective associations
Certain colors are strongly associated with certain adjectives (e.g. red is hot, blue is cold). Some of these associations are grounded in visual experiences like seeing hot embers glow red. Surprisingly, many congenitally blind people show similar color associations, despite lacking all visual experience of color. Presumably, they learn these associations via language. Can we detect these associations in the statistics of language? And if so, what form do they take? We apply a projection method to word embeddings trained on corpora of spoken and written text to identify color-adjective associations as they are represented in language. We show that these projections are predictive of color-adjective ratings collected from blind and sighted people, and that the effect size depends on the training corpus. Finally, we examine how color-adjective associations might be represented in language by training word embeddings on corpora from which various sources of color-semantic information are removed.
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- Award ID(s):
- 2020969
- NSF-PAR ID:
- 10302089
- Date Published:
- Journal Name:
- Proceedings of the Annual Meeting of the Cognitive Science Society
- Volume:
- 43
- Page Range / eLocation ID:
- 2671–2677
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.31234/osf.io/vyxpq
