  We describe a strategy for identifying the universe of research publications
relevant to the application and development of artificial intelligence. The
approach leverages the arXiv corpus of scientific preprints, in which authors
choose subject tags for their papers from a set defined by editors. We compose
a functional definition of AI relevance by learning these subjects from paper
metadata, and then inferring the arXiv-subject labels of papers in larger
corpora: Clarivate Web of Science, Digital Science Dimensions, and Microsoft
Academic Graph. This yields predictive classification $F_1$ scores between .75
and .86 for Natural Language Processing (cs.CL), Computer Vision (cs.CV), and
Robotics (cs.RO). For a single model that learns these and four other
AI-relevant subjects (cs.AI, cs.LG, stat.ML, and cs.MA), we see precision of
.83 and recall of .85. We evaluate the out-of-domain performance of our
classifiers against other sources of topic information and predictions from
alternative methods. We find that a supervised solution can generalize to
identify publications that belong to the high-level fields of study represented
on arXiv. This offers a method for identifying AI-relevant publications that
updates at the pace of research output, without reliance on subject-matter
experts for query development or labeling.
