The reply from Andrey Kutuzov via google groups felt satisfactory
I would say that word2vec algorithms are based on both.
When people say distributional representation
, they usually mean the
linguistic aspect: meaning is context, know the word by its company and
other famous quotes.
But when people say distributed representation
, it mostly doesn't have
anything to do with linguistics. It is more about computer science
aspect. If I understand Mikolov and other correctly, the word
distributed
in their papers means that each single component of a
vector representation does not have any meaning of its own. The
interpretable features (for example, word contexts in case of word2vec)
are hidden and distributed
among uninterpretable vector components:
each component is responsible for several interpretable features, and
each interpretable feature is bound to several components.
So, word2vec (and doc2vec) uses distributed representations technically,
as a way to represent lexical semantics. And at the same time it is
conceptually based on distributional hypothesis: it works only because
distributional hypothesis is true (word meanings do correlate with their
typical contexts).
But of course often the terms distributed
and distributional
are
used interchangeably, increasing misunderstanding :)