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.

本文介绍了一个商业知识发现平台的术语提取子系统，采用全面且无监督注释器来跨越高度技术领域。该注释器通过将子词分词的新颖形态信号与使用通用领域预训练句子编码器计算的术语和主题以及内部术语相似度度量相结合，提取术语。通过在大型未标记语料库上运行 UA 生成的训练数据进行变压器模型的微调或预训练，使得此设置能够提高预测性能，同时减少 CPU 和 GPU 的推理延迟。该注释器为所有未标注语料库提供了非常有竞争力的基线。