Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains

arXiv:2401.13129v2 Announce Type: replace
Abstract: Accurately typing entity mentions from text segments is a fundamental task for various natural language processing applications. Many previous approaches rely on massive human-annotated data to perform entity typing. Nevertheless, collecting such data in highly specialized science and engineering domains (e.g., software engineering and security) can be time-consuming and costly, without mentioning the domain gaps between training and inference data if the model needs to be applied to confidential datasets. In this paper, we study …

abstract annotated data applications arxiv cs.cl cs.se data domains engineering fine-grained human language language processing massive natural natural language natural language processing processing science security seed software software engineering text type typing