  In comparative linguistics, colexification refers to the phenomenon of a
lexical form conveying two or more distinct meanings. Existing work on
colexification patterns relies on annotated word lists, limiting scalability
and usefulness in NLP. In contrast, we identify colexification patterns of more
than 2,000 concepts across 1,335 languages directly from an unannotated
parallel corpus. We then propose simple and effective methods to build
multilingual graphs from the colexification patterns: ColexNet and ColexNet+.
ColexNet's nodes are concepts and its edges are colexifications. In ColexNet+,
concept nodes are additionally linked through intermediate nodes, each
representing an ngram in one of 1,334 languages. We use ColexNet+ to train
$\overrightarrow{\mbox{ColexNet+}}$, high-quality multilingual embeddings that
are well-suited for transfer learning. In our experiments, we first show that
ColexNet achieves high recall on CLICS, a dataset of crosslingual
colexifications. We then evaluate $\overrightarrow{\mbox{ColexNet+}}$ on
roundtrip translation, sentence retrieval and sentence classification and show
that our embeddings surpass several transfer learning baselines. This
demonstrates the benefits of using colexification as a source of information in
multilingual NLP.
