Semantic-aware cross-granularity contrastive learning for Chinese named entity recognition
Year: 2026
Type: article
Abstract: Chinese Named Entity Recognition (CNER) aims to identify and classify entity mentions from Chinese text and serves as a fundamental task for many downstream natural language processing applications. Recently, word-character lattice frameworks have been widely used to incorporate word-level information into CNER models. However, existing methods typically integrate lexical information through dedicated encoder architectures, such as Flat-Lattice Transformer , while overlooking the complex interactions across different granularities between character-level and word-level representations. Consequently, these methods do not fully exploit lexical knowledge and semantic dependencies, particularly their interactions with character representations. In this paper, we propose A noveL cross-granulariTy contrAstive learnIng network (ALTAI), which integrates lexical and semantic knowledge at multiple granularities. Specifically, ALTAI employs a Cross Transformer to explicitly model interactions among character representations, word-level lexical representations, and semantic representations, enabling effective information exchange across different granularities. Meanwhile, Cross-Granularity Contrastive Learning is introduced to align character representations with their corresponding lexical and semantic views, encouraging consistent representations across character, word, and semantic spaces. Together, these components produce discriminative entity representations by modeling cross-granularity interactions. Experiments on four benchmark datasets show that ALTAI consistently outperforms strong baseline models.
Source: Complex & Intelligent Systems
Institutions: Guangzhou Maritime College, South China Normal University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
Award: G693067338
OpenAlex ID: W7204877758
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