Distantly Supervised Relation Extraction based on Non-taxonomic Relation and Self-Optimization
Zhaorui Jian, Shengquan Liu, Wei Gao, Jianming Cheng · 2024
Distantly supervised relation extraction (DS-RE) leverages existing knowledge bases to generate annotated data for relation extraction (RE), addressing the issue of scarce labeled data. However, distant supervision (DS) is often limited by coarse annotations and insufficient contextual awareness, leading to relational ambiguity and introducing noise in the labeled results. Moreover, although one can optimize the classifiers in DS-RE models through weight updates, the static nature of the guiding rules for such adjustments often falls short when addressing the challenges posed by diverse non-taxonomic relations and complex noise patterns in datasets. In this paper, we propose a DS-RE framework that capitalizes on non-taxonomic relations and a self-optimizing mechanism. We define a set of consistent DS relation candidates and combine DS with a LLM to enhance the perception of entities’ contextual states during the DS process. Then, we design a Self-Optimizing Ontology-Enhanced Non-taxonomic Relation Extraction Model (SO-NRE). The model incorporates additional entity-relation knowledge to enhance the semantic depth of Non-taxonomic relation ontologies and uses an adaptive dynamic scheduling mechanism to refine the classification strategy through iterations informed by self-perception outcomes. The experimental results show that the improved DS annotation workflow has enhanced accuracy, and SO-NRE outperforms mainstream baselines in RE performance.