Towards Fine-Grained Concept Generation

Constructing large-scale taxonomies are crucial for many knowledge-rich applications that need concepts to better understand texts. However, current taxonomies suffer from the scarcity of concepts. Specifically, many fine-grained concepts are missing, while these fine-grained concepts play important roles in understanding related instances more deeply. In this paper, we propose an unsupervised fine-grained concept generation framework called FGCGen, which takes advantages of knowledge bases to generate mass of fine-grained concepts. Specifically, instead of extracting concepts from corpus, FGCGen detects entity heads and modifiers from knowledge bases and combines them to generate fine-grained concepts. We identify critical challenges of this generation process and employ three novel modules to solve them. We evaluate proposed methods on both Chinese and English datasets to show the strength of FGCGen, especially on constructing large-scale high-quality fine-grained taxonomies. Extensive experiments are introduced to prove the efficiency and effectiveness of the modules in FGCGen.