  Lexical ambiguity is widespread in language, allowing for the reuse of
economical word forms and therefore making language more efficient. If
ambiguous words cannot be disambiguated from context, however, this gain in
efficiency might make language less clear -- resulting in frequent
miscommunication. For a language to be clear and efficiently encoded, we posit
that the lexical ambiguity of a word type should correlate with how much
information context provides about it, on average. To investigate whether this
is the case, we operationalise the lexical ambiguity of a word as the entropy
of meanings it can take, and provide two ways to estimate this -- one which
requires human annotation (using WordNet), and one which does not (using BERT),
making it readily applicable to a large number of languages. We validate these
measures by showing that, on six high-resource languages, there are significant
Pearson correlations between our BERT-based estimate of ambiguity and the
number of synonyms a word has in WordNet (e.g. $\rho = 0.40$ in English). We
then test our main hypothesis -- that a word's lexical ambiguity should
negatively correlate with its contextual uncertainty -- and find significant
correlations on all 18 typologically diverse languages we analyse. This
suggests that, in the presence of ambiguity, speakers compensate by making
contexts more informative.
