Recognition Service for Named Entities via Multilayer Feature Learning for Large Web Knowledge Bases

Chan Li, Rui Li, Yinru Ma, Xinkui Zhao, Lei Hei, Yuyu Yin, Yueshen Xu, Yi Yang

Abstract: In the field of Web knowledge base mining and Web services, the recognition service for named entity faces many challenges such as context complexity, semantic subtlety, and fuzzy entity boundaries, all of which require highly accurate and robust recognition service. The current services usually fail to reach those conditions. To address this issue, this paper proposes a recognition service for named entities for large Web knowledge bases, and the core contrition is the developed dual multilayer feature learning (D-MLFL) service, which combines projected gradient descent (PGD), adversarial learning, and a fused attention mechanism. Our service successfully addresses the challenges of complex context and subtle semantics faced by named entity recognition tasks. Our service integrates deep language models, recurrent neural networks, and conditional random fields, and clearly outperforms existing approaches in many sub-tasks, including in feature extraction, sequence modeling, and label decoding, especially in dealing with complex and diverse entity types and contextual relationships. We performed sufficient experiments, and the results show that our service significantly enhances the robustness against noise and abnormal Web data. The ability to extract entity features is improved, resulting in higher accuracy in identifying entities with fuzzy boundaries and complex semantics.

External IDs:dblp:conf/icws/LiLMZHYXY25