Fine Grained Entity Typing

Fine-grained entity typing (FET) aims to accurately classify entities mentioned in text into highly specific semantic categories, going beyond broad classifications. Current research focuses on overcoming the challenges of limited annotated data by exploring techniques like seed-guided learning, fine-tuning pre-trained language models (PLMs) with ultra-fine-grained data, and correcting noisy labels through co-prediction or other robust methods. These advancements are crucial for improving knowledge extraction from unstructured text, enabling more precise information retrieval and facilitating applications in diverse fields like scientific literature analysis and knowledge graph construction.