  Neural embeddings are a popular set of methods for representing words,
phrases or text as a low dimensional vector (typically 50-500 dimensions).
However, it is difficult to interpret these dimensions in a meaningful manner,
and creating neural embeddings requires extensive training and tuning of
multiple parameters and hyperparameters. We present here a simple unsupervised
method for representing words, phrases or text as a low dimensional vector, in
which the meaning and relative importance of dimensions is transparent to
inspection. We have created a near-comprehensive vector representation of
words, and selected bigrams, trigrams and abbreviations, using the set of
titles and abstracts in PubMed as a corpus. This vector is used to create
several novel implicit word-word and text-text similarity metrics. The implicit
word-word similarity metrics correlate well with human judgement of word pair
similarity and relatedness, and outperform or equal all other reported methods
on a variety of biomedical benchmarks, including several implementations of
neural embeddings trained on PubMed corpora. Our implicit word-word metrics
capture different aspects of word-word relatedness than word2vec-based metrics
and are only partially correlated (rho = ~0.5-0.8 depending on task and
corpus). The vector representations of words, bigrams, trigrams, abbreviations,
and PubMed title+abstracts are all publicly available from
http://arrowsmith.psych.uic.edu for release under CC-BY-NC license. Several
public web query interfaces are also available at the same site, including one
which allows the user to specify a given word and view its most closely related
terms according to direct co-occurrence as well as different implicit
similarity metrics.
