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Yanping Chen

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12 papers

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AAAI Conference 2026 Conference Paper

A Reasoning Paradigm for Named Entity Recognition

Hui Huang
Yanping Chen
Ruizhang Huang
Chuan Lin
Yongbin Qin

Generative LLMs typically improve Named Entity Recognition (NER) performance through instruction tuning. They excel at generating entities by semantic pattern matching but lack an explicit, verifiable reasoning mechanism. This "cognitive shortcutting" leads to suboptimal performance and weak generalization, especially in zero-shot and low-resource scenarios where reasoning from limited contextual cues is crucial. To address this issue, a reasoning framework is proposed for NER, which shifts the extraction paradigm from implicit pattern matching to explicit reasoning. This framework consists of three stages: Chain of Thought (CoT) generation, CoT tuning, and reasoning enhancement. First, a dataset annotated with NER-oriented CoTs is generated, which contain task-relevant reasoning chains. Then, they are used to tune the NER model to generate coherent rationales before deriving the final answer. Finally, a reasoning enhancement stage is implemented to optimize the reasoning process using a comprehensive reward signal. This stage ensures explicit and verifiable extractions. Experiments show that ReasoningNER demonstrates impressive cognitive ability in the NER task, achieving competitive performance. In zero-shot settings, it achieves SoTA performance, outperforming GPT-4 by 12.3 percentage points on the F1 score. Analytical results demonstrate its great potential to advance research in reasoning-oriented information extraction.

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AAAI Conference 2026 Conference Paper

Active Multi-source Domain Adaptation for Multimodal Fake News Detection

Yanping Chen
Weijie Shi
Mengze Li
Yue Cui
Jiaming Li
Ruiyuan Zhang
Hao Chen
Hanghui Guo

Multimodal fake news detection plays a crucial role in combating online misinformation. The inherent domain diversity of news in the real world has driven the development of cross-domain detection methods. However, these detection methods either suffer from significant performance degradation due to semantic and deception pattern shifts between the training (source) and test (target) domains or heavily rely on annotated labels. To address the problems, we propose ADOSE, an active multi-source domain adaptation framework for multimodal fake news detection which actively annotates a small subset of target samples to improve detection performance. Specifically, for domain shifts, we design a multi-expert classifier network based on refined features to comprehensively capture and adapt to the semantic space and deception patterns of news across different domains. To maximize adaptation performance with limited annotation cost, we propose a least-disagree uncertainty selector equipped with a diversity calculator for selecting the most informative samples. The selector leverages the uncertainty of inconsistent predictions before and after perturbations by multiple classifiers as an indicator of unfamiliar samples. It further incorporates diversity scores derived from multi-view features to ensure the chosen samples achieve maximal coverage of target domain features. The extensive experiments on multiple datasets show that ADOSE outperforms existing domain adaptation methods by 2.45% ~ 9.1%, indicating the superiority of our model.

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EAAI Journal 2025 Journal Article

ESIGCF: Extremely simplified but intent-enhanced graph collaborative filtering for recommendation

Zhi Yang
Ruizhang Huang
Yanping Chen
Chuan Lin
Yongbin Qin

Graph convolutional networks (GCNs) and graph contrastive learning (GCL) have substantially advanced recommender systems by modeling high-order user–item interactions and leveraging self-supervised signals. However, many existing methods overemphasize user–user or item–item similarities and rely on complex intent modeling, leading to increased complexity and limited exposure to diverse items. To address these challenges, we propose ESIGCF (Extremely Simplified but Intent-enhanced Graph Collaborative Filtering) — a lightweight yet effective recommendation framework. ESIGCF explicitly defines user intent as the inner product between user and item embedding vectors and comprises two primary modules: (i) an intent-enhanced GCN that uses hybrid normalization (combining mean- and symmetric-normalization) to capture fine-grained user–item preferences without additional intent parameters, and (ii) an intent-aware GCL that aligns user–item pairs and positive and generated negative items. Negative samples are generated via a non-linear activation of item embedding interactions, promoting exposure to varied candidates without data augmentation. Experiments on three public datasets (Alibaba-iFashion, Yelp2018, Amazon-Book) show that ESIGCF consistently outperforms state-of-the-art baselines. For instance, on Alibaba-iFashion, ESIGCF achieves Recall@20 of 0. 1273 versus 0. 1059 for the best intent-enhanced baseline (a 20. 2% relative improvement). Comprehensive experiments confirm that ESIGCF effectively captures latent user intent, mitigates popularity bias, and enhances recommendation performance with reduced complexity. Our code is available at https: //github. com/Yangzhi22/ESIGCF.

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JBHI Journal 2025 Journal Article

Gestalt-Inspired Feature Integration Network with Entropy Uncertainty Modeling for Pathology Image Segmentation

Dawei Fan
Jiamei Wen
Heng Dong
Mingyue Han
Jun Li
Chengfei Cai
Yanping Chen
Changcai Yang

The accuracy and stability of pathology image segmentation have become critical factors in clinical applications such as cancer screening and tumor grading. However, the presence of complex local structures, uncertain regions, and subtle morphological variations in pathological images continues to pose significant challenges. Most existing feature fusion approaches rely on the simplistic aggregation of extracted features, neglecting the unique characteristics and relative importance of distinct feature representations, which ultimately limits their potential to enhance model performance. To address these issues, we propose a Gestalt-Inspired Feature Integration Network (GeNet), a novel architecture inspired by Gestalt theory that mirrors the human visual system's ability to derive holistic understanding from partial information. Embracing the principle that ‘the whole is greater than the sum of its parts, ’ GeNet introduces a mechanism to synergistically leverage multi-scale information, which assesses the similarity between features to achieve a more meaningful fusion of global context and local detail. Given the variability in target appearance within pathological images, we use information entropy to quantify feature uncertainty, allowing the model to prioritize uncertain regions and reduce the occurrence of ambiguous results. To explicitly eliminate multi-feature redundancy and misalignment, the refinement block utilizes parallel convolutional recalibration to fully leverage the advantages of various features. Extensive experiments on multiple pathological image segmentation datasets, including GlaS, GCaSeg, and EBHI-Seg, demonstrate that GeNet achieves high accuracy and strong robustness, offering a new perspective for joint modeling of global and local features in medical image analysis.

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AIIM Journal 2025 Journal Article

Multiplex aggregation combining sample reweight composite network for pathology image segmentation

Dawei Fan
Zhuo Chen
Yifan Gao
Jiaming Yu
Kaibin Li
Yi Wei
Yanping Chen
Riqing Chen

In digital pathology, nuclei segmentation is a critical task for pathological image analysis, holding significant importance for diagnosis and research. However, challenges such as blurred boundaries between nuclei and background regions, domain shifts between pathological images, and uneven distribution of nuclei pose significant obstacles to segmentation tasks. To address these issues, we propose an innovative Causal inference inspired Diversified aggregation convolution Network named CDNet, which integrates a Diversified Aggregation Convolution (DAC), a Causal Inference Module (CIM) based on causal discovery principles, and a comprehensive loss function. DAC improves the issue of unclear boundaries between nuclei and background regions, and CIM enhances the model’s cross-domain generalization ability. A novel Stable-Weighted Combined loss function was designed that combined the chunk-computed Dice Loss with the Focal Loss and the Causal Inference Loss to address the issue of uneven nuclei distribution. Experimental evaluations on the MoNuSeg, GLySAC, and MoNuSAC datasets demonstrate that CDNet significantly outperforms other models and exhibits strong generalization capabilities. Specifically, CDNet outperforms the second-best model by 0. 79% (mIoU) and 1. 32% (DSC) on the MoNuSeg dataset, by 2. 65% (mIoU) and 2. 13% (DSC) on the GLySAC dataset, and by 1. 54% (mIoU) and 1. 10% (DSC) on the MoNuSAC dataset. Code is publicly available at https: //github. com/7FFDW/CDNet.

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AIJ Journal 2024 Journal Article

A multi-graph representation for event extraction

Hui Huang
Yanping Chen
Chuan Lin
Ruizhang Huang
Qinghua Zheng
Yongbin Qin

Event extraction has a trend in identifying event triggers and arguments in a unified framework, which has the advantage of avoiding the cascading failure in pipeline methods. The main problem is that joint models usually assume a one-to-one relationship between event triggers and arguments. It leads to the argument multiplexing problem, in which an argument mention can serve different roles in an event or shared by different events. To address this problem, we propose a multigraph-based event extraction framework. It allows parallel edges between any nodes, which is effective to represent semantic structures of an event. The framework enables the neural network to map a sentence(s) into a structurized semantic representation, which encodes multi-overlapped events. After evaluated on four public datasets, our method achieves the state-of-the-art performance, outperforming all compared models. Analytical experiments show that the multigraph representation is effective to address the argument multiplexing problem and helpful to advance the discriminability of the neural network for event extraction.

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AILAW Journal 2024 Journal Article

Self-training improves few-shot learning in legal artificial intelligence tasks

Yulin Zhou
Yongbin Qin
Ruizhang Huang
Yanping Chen
Chuan Lin
Yuan Zhou

Abstract As the labeling costs in legal artificial intelligence tasks are expensive. Therefore, it becomes a challenge to utilize low cost to train a robust model. In this paper, we propose a LAIAugment approach, which aims to enhance the few-shot learning capability in legal artificial intelligence tasks. Specifically, we first use the self-training approach to label the amount of unlabelled data to enhance the feature learning capability of the model. Moreover, we also search for datasets that are similar to the training set by improving the text similarity function. We conducted experimental analyses for three legal artificial intelligence tasks, including evidence extraction, legal element extraction, and case multi-label prediction, which composed of 3500 judgement documents. The experimental results show that the proposed LAIAugment method has an average F1-score of 72. 3% on the three legal AI tasks, which is 1. 93% higher than the baseline model. At the same time, it shows a huge improvement in few-shot learning.

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AILAW Journal 2023 Journal Article

A novel MRC framework for evidence extracts in judgment documents

Yulin Zhou
Lijuan Liu
Yanping Chen
Ruizhang Huang
Yongbin Qin
Chuan Lin

Abstract Evidences are important proofs to support judicial trials. Automatically extracting evidences from judgement documents can be used to assess the trial quality and support “Intelligent Court”. Current evidence extraction is primarily depended on sequence labelling models. Despite their success, they can only assign a label to a token, which is difficult to recognize nested evidence entities in judgment documents, where a token may belong to several evidences at the same time. In this paper, we present a novel evidence extraction architecture called ATT-MRC, in which extracting evidence entities is formalized as a question answer problem, where all evidence spans are screened out as possible correct answers. Furthermore, to address the data imbalance problem in the judgement documents, we revised the loss function and combined it with a data enhancement technique. Experimental results demonstrate that our model has better performance than related works in evidence extraction.

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IS Journal 2020 Journal Article

Recognizing Nested Named Entity Based on the Neural Network Boundary Assembling Model

Yanping Chen
Yuefei Wu
Yongbin Qin
Ying Hu
Zeyu Wang
Ruizhang Huang
Xinyu Cheng
Ping Chen

The task to recognize named entities is often modeled as a sequence labeling process, which selects a label path whose probability is maximum for an input sentence. Because it makes the assumption that the input sentence has a flattened structure, it often fails to recognize nested named entities. In our previous work, a boundary assembling (BA) model was proposed. It is a cascading framework, which identifies named entity boundaries first, and then assembles them into entity candidates for further assessment. This model is effective to recognize nested named entities, but still suffers from poor performance caused by the sparse feature problem. In this article, the BA model is remodeled with the advancement of neural networks, which enables the model to capture semantic information of a sentence by using word embeddings pretrained in external resources. In our experiments, it shows an impressive improvement on the final performance, outperforming the state of the art more than 17% in F-score.

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IS Journal 2017 Journal Article

A Set Space Model for Feature Calculus

Yanping Chen
Qinghua Zheng
Ping Chen

Processing natural language at the sentence level suffers from a sparse-feature problem caused by the limited number of words in a sentence. In this article, a Set Space Model (SSM) is proposed to utilize sentence information, the main idea being that, depending on structural characteristics or functional principles of linguistics, features in a sentence can be grouped into different sets. Feature calculus can then operate on the grouped features and capture structural information using external knowledge. The authors implement this method in a traditional information extraction task, with results showing significant and constant improvement in general information extraction.

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IS Journal 2015 Journal Article

A Boundary Assembling Method for Chinese Entity-Mention Recognition

Yanping Chen
Qinghua Zheng
Ping Chen

A boundary assembling (BA) method is presented for Chinese entity-mention recognition. Given a sentence, instead of recognizing entity mentions in a unitary style, the authors' BA method first detects boundaries of entity mentions and then assembles detected boundaries into entity-mention candidates. Each candidate is further assessed by a classifier trained on nonlocal features. This method can make better use of nonlocal features and effectively recognize nested entity mentions. Using the ACE 2005 Chinese corpus, the authors' experimental results show an improvement over state-of-the-art techniques, outperforming existing methods in F-score by 5 percent for entity-mention detection and 4. 23 percent for entity-mention recognition.

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AIJ Journal 2015 Journal Article

Feature assembly method for extracting relations in Chinese

Yanping Chen
Qinghua Zheng
Ping Chen

The goal of relation extraction is to detect relations between two entities in free text. In a sentence, a relation instance usually comprises a small number of words, which yields a sparse feature representation. To make better use of limited information in a relation instance, parsing trees and combined features are employed widely to capture the local dependencies of relation instances. However, the performance of parsing tree-based systems is often degraded by chunking or parsing errors. Combined features are used widely, but few studies have addressed how features can be combined to achieve optimal performance. Thus, in this study, we propose a feature assembly method for relation extraction. Six types of candidate features (head noun, POS tag, n-gram, omni-word, etc.) are employed as atomic features and six constraint conditions (singleton, position, syntax, etc.) are used to combine these features in different settings. Depending on the utilization of candidate features, different constraint conditions can be explored to achieve the optimal extraction performance. Our method is effective for capturing local dependencies and it reduces the errors caused by inaccurate parsing. We tested the proposed method using the ACE 2005 Chinese and English corpora, and it achieved state-of-the-art performance, where it was significantly superior to existing methods.

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