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Fine-grained entity typing (FET), which assigns entities in text with context-sensitive, fine-grained semantic types, is a basic but important task for knowledge extraction from unstructured text. FET has been studied extensively in natural language processing and typically relies on human-annotated corpora for training, which is costly and difficult to scale. Recent studies explore the utilization of pre-trained language models (PLMs) as a knowledge base to generate rich and context-aware weak supervision for FET. However, a PLM still requires direction and guidance to serve as a knowledge base as they often generate a mixture of rough and fine-grained types, or tokens unsuitable for typing. In this study, we vision that an ontology provides a semantics-rich, hierarchical structure, which will help select the best results generated by multiple PLM models and head words. Specifically, we propose a novel annotation-free, ontology-guided FET method, ONTOTYPE, which follows a type ontological structure, from coarse to fine, ensembles multiple PLM prompting results to generate a set of type candidates, and refines its type resolution, under the local context with a natural language inference model. Our experiments on the Ontonotes, FIGER, and NYT datasets using their associated ontological structures demonstrate that our method outperforms the state-of-the-art zero-shot fine-grained entity typing methods as well as a typical LLM method, ChatGPT. Our error analysis shows that refinement of the existing ontology structures will further improve fine-grained entity typing. 

Students’ experience with software testing in undergraduate computing courses is often relatively shallow, as compared to the importance of the topic. This experience report describes introducing industrial-strength testing into CMPSC 156, an upper division course in software engineering at UC Santa Barbara . We describe our efforts to modify our software engineering course to introduce rigorous test-coverage requirements into full-stack web development projects, requirements similar to those the authors had experienced in a professional software development setting. We present student feedback on the course and coverage metrics for the projects. We reflect on what about these changes worked (or didn’t), and provide suggestions for other instructors that would like to give their students a deeper experience with software testing in their software engineering courses.