  Term extraction is an information extraction task at the root of knowledge
discovery platforms. Developing term extractors that are able to generalize
across very diverse and potentially highly technical domains is challenging, as
annotations for domains requiring in-depth expertise are scarce and expensive
to obtain. In this paper, we describe the term extraction subsystem of a
commercial knowledge discovery platform that targets highly technical fields
such as pharma, medical, and material science. To be able to generalize across
domains, we introduce a fully unsupervised annotator (UA). It extracts terms by
combining novel morphological signals from sub-word tokenization with
term-to-topic and intra-term similarity metrics, computed using general-domain
pre-trained sentence-encoders. The annotator is used to implement a
weakly-supervised setup, where transformer-models are fine-tuned (or
pre-trained) over the training data generated by running the UA over large
unlabeled corpora. Our experiments demonstrate that our setup can improve the
predictive performance while decreasing the inference latency on both CPUs and
GPUs. Our annotators provide a very competitive baseline for all the cases
where annotations are not available.
