Semi-supervised generative open information extraction - Zhou, Shaowen (Nanyang Technological University, 2023)
Open Information Extraction (OpenIE) extracts facts in the form of n-ary relation tuples, i.e., (arg1, predicate, arg2, …, argn), from unstructured text without relying on predefined ontology schema. It has the potential to handle heterogeneous corpora with minimal human intervention. With OpenIE, Web-scale unconstrained IE systems can be developed to acquire large quantities of knowledge. The knowledge gathered can then be integrated and used in various natural language processing (NLP) applications, such as textual entailment, summarization, question answering, and explicit reasoning.