Sequence labeling remains a significant challenge in low-resource, domain-specific scenarios, particularly for character-dense languages.Existing methods primarily focus on enhancing model comprehension and improving data diversity to boost performance.However, these approaches still struggle with inadequate model applicability and semantic distribution biases in domain-specific contexts.To overcome these limitations, we propose a novel framework that combines an LLM-based knowledge enhancement workflow with a span-based Knowledge Fusion for Rich and Efficient Extraction (KnowFREE) model 1 .Our workflow employs explanation prompts to generate precise contextual interpretations of target entities, effectively mitigating semantic biases and enriching the model's contextual understanding.The KnowFREE model further integrates extension label features, enabling efficient nested entity extraction without relying on external knowledge during inference.Experiments on multiple domain-specific sequence labeling datasets demonstrate that our approach achieves stateof-the-art performance, effectively addressing the challenges posed by low-resource settings.

Unsupervised sentence embedding methods based on contrastive learning have gained attention for effectively representing sentences in natural language processing. Retrieving additional samples via a nearest-neighbor approach can enhance the model’s ability to learn relevant semantics and distinguish sentences. However, previous related research mainly focused on retrieving neighboring samples within a single batch range or global range, which makes the model possibly unable to capture effective semantic information or incurs excessive time cost. Furthermore, previous methods use retrieved neighbor samples as hard negatives. We argue that nearest neighbor samples contain relevant semantic information, and treating them as hard negatives risks losing valuable semantic knowledge. In this work, we introduce Neighbor Contrastive learning for unsupervised Sentence Embeddings(NCSE), which combines contrastive learning with the nearest-neighbor approach. Specifically, we create a candidate set to store sentence embeddings across multiple batches. Retrieving the candidate set can ensure sufficient samples, making it easier for the model to learn relevant semantics. Using retrieved nearest neighbor samples as positives and applying the self-attention mechanism to aggregate the sample and its neighbors encourages the model to learn relevant semantics from multiple neighbors. Experiments on the semantic text similarity task demonstrate our method’s effectiveness in sentence embedding learning.

Abstract Knowledge graph (KG) has been fully considered in natural language generation (NLG) tasks. A KG can help models generate controllable text and achieve better performance. However, most existing related approaches still lack explainability and scalability in large‐scale knowledge reasoning. In this work, we propose a novel CogNLG framework for KG‐to‐text generation tasks. Our CogNLG is implemented based on the dual‐process theory in cognitive science. It consists of two systems: one system acts as the analytic system for knowledge extraction, and another is the perceptual system for text generation by using existing knowledge. During text generation, CogNLG provides a visible and explainable reasoning path. Our framework shows excellent performance on all datasets and achieves a BLEU score of 36.7, which increases by 6.7 compared to the best competitor.

Achieving good performance on few-shot or zero-shot datasets has been a long-term challenge for NER. The conventional semantic transfer approaches on NER will decrease model performance when the semantic distribution is quite different, especially in Chinese few-shot NER. Recently, prompt-tuning has been thoroughly considered for low-resource tasks. But there is no effective prompt-tuning approach for Chinese few-shot NER. In this work, we propose a prompt-based Parent and Child BERT (PCBERT) for Chinese few-shot NER. To train an annotating model on high-resource datasets and then discover more implicit labels on low-resource datasets. We further design a label extension strategy to achieve label transferring from high-resource datasets. We evaluated our model on Weibo and the other three sampling Chinese NER datasets, and the experimental result demonstrates our approach’s effectiveness in few-shot learning.