  Word embeddings are a popular way to improve downstream performances in
contemporary language modeling. However, the underlying geometric structure of
the embedding space is not well understood. We present a series of explorations
using bio-inspired methodology to traverse and visualize word embeddings,
demonstrating evidence of discernible structure. Moreover, our model also
produces word similarity rankings that are plausible yet very different from
common similarity metrics, mainly cosine similarity and Euclidean distance. We
show that our bio-inspired model can be used to investigate how different word
embedding techniques result in different semantic outputs, which can emphasize
or obscure particular interpretations in textual data.
