Mitigating Out-of-Entity Errors in Named Entity Recognition: A Sentence-Level Strategy

2024-12-11

AI Summary

Named entity recognition (NER) suffers significant performance degradation on out-of-entity (OOE) instances—entities unseen during training. To address this, we propose S+NER, a framework emphasizing sentence-level contextual modeling to enhance generalization. Our key contributions are threefold: (1) the first template-guided sentence-level contrastive learning objective, explicitly aligning semantically similar sentence pairs; (2) a template pooling mechanism that deeply integrates pre-trained language model sentence representations into the NER decoding process; and (3) a sentence-level representation refinement module that strengthens boundary detection and entity type discrimination. Evaluated on five OOE-NER benchmark datasets, S+NER consistently outperforms state-of-the-art methods, demonstrating the effectiveness and robustness of sentence-level semantic modeling for recognizing rare and unseen entities.

Technology Category

• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Search and Optimization: Learning to Search
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Many previous models of named entity recognition (NER) suffer from the problem of Out-of-Entity (OOE), i.e., the tokens in the entity mentions of the test samples have not appeared in the training samples, which hinders the achievement of satisfactory performance. To improve OOE-NER performance, in this paper, we propose a new framework, namely S+NER, which fully leverages sentence-level information. Our S+NER achieves better OOE-NER performance mainly due to the following two particular designs. 1) It first exploits the pre-trained language model's capability of understanding the target entity's sentence-level context with a template set. 2) Then, it refines the sentence-level representation based on the positive and negative templates, through a contrastive learning strategy and template pooling method, to obtain better NER results. Our extensive experiments on five benchmark datasets have demonstrated that, our S+NER outperforms some state-of-the-art OOE-NER models.

Problem

• Entity Recognition
• Unseen Entities
• Accuracy Improvement

Innovation

• S+NER
• Pre-trained Language Model
• Optimized Representation Learning