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学者姓名：王一蕾
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Abstract ：
Quantum-inspired models have demonstrated superior performance in many downstream language tasks, such as question answering and sentiment analysis. However, recent models primarily focus on embedding and measurement operations, overlooking the significance of the quantum evolution process. In this work, we present a novel quantum-inspired neural network, LI-QiLM, which integrates the Lindblad Master Equation (LME) to model the evolution process and the interferometry to the measurement process, providing more physical meaning to strengthen the interpretability. We conduct comprehensive experiments on six sentiment analysis datasets. Compared to the traditional neural networks, transformer-based pre-trained models and quantum-inspired models, such as CICWE-QNN and ComplexQNN, the proposed method demonstrates superior performance in accuracy and F1-score on six commonly used datasets for sentiment analysis. Additional ablation tests verify the effectiveness of LME and interferometry. © 2024 Association for Computational Linguistics.
Keyword ：
Computational linguistics Computational linguistics Interferometry Interferometry Sentiment analysis Sentiment analysis
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| GB/T 7714 | Yan, Kehuan , Lai, Peichao , Wang, Yilei . Quantum-inspired Language Model with Lindblad Master Equation and Interference Measurement for Sentiment Analysis [C] . 2024 : 2112-2121 . |
| MLA | Yan, Kehuan 等. "Quantum-inspired Language Model with Lindblad Master Equation and Interference Measurement for Sentiment Analysis" . (2024) : 2112-2121 . |
| APA | Yan, Kehuan , Lai, Peichao , Wang, Yilei . Quantum-inspired Language Model with Lindblad Master Equation and Interference Measurement for Sentiment Analysis . (2024) : 2112-2121 . |
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Abstract ：
Quantum-inspired models have demonstrated superior performance in many downstream language tasks, such as question answering and sentiment analysis. However, recent models primarily focus on embedding and measurement operations, overlooking the significance of the quantum evolution process. In this work, we present a novel quantum-inspired neural network, LI-QiLM, which integrates the Lindblad Master Equation (LME) to model the evolution process and the interferometry to the measurement process, providing more physical meaning to strengthen the interpretability. We conduct comprehensive experiments on six sentiment analysis datasets. Compared to the traditional neural networks, transformer-based pre-trained models and quantum-inspired models, such as CICWE-QNN and ComplexQNN, the proposed method demonstrates superior performance in accuracy and F1-score on six commonly used datasets for sentiment analysis. Additional ablation tests verify the effectiveness of LME and interferometry. © 2024 Association for Computational Linguistics.
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| GB/T 7714 | Yan, K. , Lai, P. , Wang, Y. . Quantum-inspired Language Model with Lindblad Master Equation and Interference Measurement for Sentiment Analysis [未知]. |
| MLA | Yan, K. 等. "Quantum-inspired Language Model with Lindblad Master Equation and Interference Measurement for Sentiment Analysis" [未知]. |
| APA | Yan, K. , Lai, P. , Wang, Y. . Quantum-inspired Language Model with Lindblad Master Equation and Interference Measurement for Sentiment Analysis [未知]. |
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Abstract ：
提出一个改进的基于生成对抗网络的协同过滤(CFGAN)的模型,通过引入增强的置换注意力机制,强化其面向稀疏数据集的特征聚焦能力,并考虑用户可能交互物品对推荐结果的影响.此外,将协同用户社交网络从用户反馈中提取的语义好友特征嵌入CFGAN,以实现负样本的个性化抽取,进一步提升模型面向稀疏数据场景的推荐效果.
Keyword ：
个性化推荐 个性化推荐 协同用户社交网络 协同用户社交网络 数据稀疏 数据稀疏 生成对抗网络 生成对抗网络 置换注意力 置换注意力
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| GB/T 7714 | 陈文婷 , 陈学勤 , 王伟津 et al. 面向稀疏数据场景的生成对抗网络推荐算法 [J]. | 福州大学学报（自然科学版） , 2023 , 51 (4) : 467-474 . |
| MLA | 陈文婷 et al. "面向稀疏数据场景的生成对抗网络推荐算法" . | 福州大学学报（自然科学版） 51 . 4 (2023) : 467-474 . |
| APA | 陈文婷 , 陈学勤 , 王伟津 , 蔡毅津 , 王一蕾 . 面向稀疏数据场景的生成对抗网络推荐算法 . | 福州大学学报（自然科学版） , 2023 , 51 (4) , 467-474 . |
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Abstract ：
Due to the problems of poor scalability, difficult experimental evaluation, and the lack of teaching data analysis and collaborative sharing in traditional experimental teaching platforms, this paper designs an interactive and scalable intelligent experimental teaching auxiliary platform BERTDS based on deep learning algorithms and computer technology. The platform provides a wide range of functions, such as the release of experimental resources, online Q&A, cloud storage sharing, automatic evaluation, similarity detection, evaluation and assignment management, etc. This paper first introduces the design idea and overall architecture of the experimental platform based on the deep learning BERT framework; then expounds the design of the organization module and automated evaluation engine that support a variety of experimental schemes and the distributed deployment scheme of the server; finally, through the actual application data analysis and user Research feedback to prove the feasibility and effectiveness of the platform. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword ：
automatic evaluation automatic evaluation BERT BERT Experiment platform Experiment platform Intelligent teaching system Intelligent teaching system
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| GB/T 7714 | Liu, W. , Wang, Y. , Fu, Y. et al. Intelligent Experimental Teaching Auxiliary Platform Based on BERT [未知]. |
| MLA | Liu, W. et al. "Intelligent Experimental Teaching Auxiliary Platform Based on BERT" [未知]. |
| APA | Liu, W. , Wang, Y. , Fu, Y. , Ye, F. , Lai, P. . Intelligent Experimental Teaching Auxiliary Platform Based on BERT [未知]. |
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The Priority Dispatching Rules (PDRs) and business solvers are widely employed for solving real-world scheduling problems, such as Job Shop Scheduling (JSSP) and JSSP with maintenance time (JSSP-MT) problems. However, the effective designs of PDRs and mathematical modelings are tedious and complex relying heavily on a myriad of specialized knowledge. In this paper, we propose an approach to automatically learn PDRs based on Graph Node Embedding (GNE) and Deep Reinforcement Learning (DRL). We describe JSSP as a disjunctive graph and utilize a GNE approach to facilitate better state embeddings. Ablation studies demonstrate the significant positive contribution of GNE both on JSSP and JSSP-MT. Experiments show that some high-quality combined PDRs can be learned with better approximate solutions against the traditional single PDRs. Solutions produced by our approach are much closer to those from mathematical solvers than previous methods. © 2023 SPIE.
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| GB/T 7714 | Fang, X. , Li, J. , Wang, Y. . Learning to Schedule Job Shop Scheduling Problem with Maintenance Time using Graph Node Embedding and Deep Reinforcement Learning [未知]. |
| MLA | Fang, X. et al. "Learning to Schedule Job Shop Scheduling Problem with Maintenance Time using Graph Node Embedding and Deep Reinforcement Learning" [未知]. |
| APA | Fang, X. , Li, J. , Wang, Y. . Learning to Schedule Job Shop Scheduling Problem with Maintenance Time using Graph Node Embedding and Deep Reinforcement Learning [未知]. |
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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.
Keyword ：
cognitive graph cognitive graph KG-to-text KG-to-text natural language generation natural language generation
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| GB/T 7714 | Lai, Peichao , Ye, Feiyang , Fu, Yanggeng et al. CogNLG: Cognitive graph for KG-to-text generation [J]. | EXPERT SYSTEMS , 2023 , 41 (1) . |
| MLA | Lai, Peichao et al. "CogNLG: Cognitive graph for KG-to-text generation" . | EXPERT SYSTEMS 41 . 1 (2023) . |
| APA | Lai, Peichao , Ye, Feiyang , Fu, Yanggeng , Chen, Zhiwei , Wu, Yingjie , Wang, Yilei et al. CogNLG: Cognitive graph for KG-to-text generation . | EXPERT SYSTEMS , 2023 , 41 (1) . |
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Abstract ：
Short answer scoring is a significant task in natural language processing. On datasets comprising numerous explicit or implicit symbols and quantization entities, the existing approaches continue to perform poorly. Additionally, the majority of relevant datasets contain few-shot samples, reducing model efficacy in low-resource scenarios. To solve the above issues, we propose a Multi-level Semantic Inference Model (M-Sim), which obtains features at multiple scales to fully consider the explicit or implicit entity information contained in the data. We then design a prompt-based data augmentation to construct the simulated datasets, which effectively enhance model performance in low-resource scenarios. Our M-Sim outperforms the best competitor models by an average of 1.48 percent in the F1 score. The data augmentation significantly increases all approaches' performance by an average of 0.036 in correlation coefficient scores.
Keyword ：
Few-shot learning Few-shot learning Short answer scoring Short answer scoring Text similarity Text similarity
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| GB/T 7714 | Lai, Peichao , Ye, Feiyang , Fu, Yanggeng et al. M-Sim: Multi-level Semantic Inference Model for Chinese short answer scoring in low-resource scenarios [J]. | COMPUTER SPEECH AND LANGUAGE , 2023 , 84 . |
| MLA | Lai, Peichao et al. "M-Sim: Multi-level Semantic Inference Model for Chinese short answer scoring in low-resource scenarios" . | COMPUTER SPEECH AND LANGUAGE 84 (2023) . |
| APA | Lai, Peichao , Ye, Feiyang , Fu, Yanggeng , Chen, Zhiwei , Wu, Yingjie , Wang, Yilei . M-Sim: Multi-level Semantic Inference Model for Chinese short answer scoring in low-resource scenarios . | COMPUTER SPEECH AND LANGUAGE , 2023 , 84 . |
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虽然基于循环神经网络（RNN）的会话推荐算法可以有效地对会话中的长期依赖关系进行建模，并且可以结合注意力机制来刻画用户在会话中的主要目的，但它在进行会话建模的过程中无法绕过与用户主要目的不相关的物品，易受其影响以致降低推荐精度。针对上述问题，设计了成对编码方案来将原始输入序列嵌入向量转化为一个三维张量表示，使得非相邻的行为也能够产生联系。通过二维卷积神经网络（CNN）来处理该张量以捕获非相邻物品间的联系，并提出了引入用于会话推荐的二维卷积神经网络的神经注意力推荐机（COS-NARM）模型。该模型能有效跳过序列中与用户主要目的不相关的物品。实验结果表明，COS-NARM模型在DIGINETICA...
Keyword ：
会话推荐 会话推荐 卷积神经网络 卷积神经网络 循环神经网络 循环神经网络 成对编码 成对编码 欧氏距离 欧氏距离
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| GB/T 7714 | 陈学勤 , 陶涛 , 张钟旺 et al. 融合成对编码方案及二维卷积神经网络的长短期会话推荐算法 [J]. | 计算机应用 , 2022 , 42 (05) : 1347-1354 . |
| MLA | 陈学勤 et al. "融合成对编码方案及二维卷积神经网络的长短期会话推荐算法" . | 计算机应用 42 . 05 (2022) : 1347-1354 . |
| APA | 陈学勤 , 陶涛 , 张钟旺 , 王一蕾 . 融合成对编码方案及二维卷积神经网络的长短期会话推荐算法 . | 计算机应用 , 2022 , 42 (05) , 1347-1354 . |
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A wealth of semantic features exist in the reviews written by users, such as rich information on item features and implicit preferences of users. Existing review-based recommendation models usually employ Convolutional Neural Networks (CNNs) to learn representations of users and items from reviews. However, these CNNs-based models suffer from two main problems: (1) they only consider the information of the word itself during the convolution, ignoring the high-order contextual semantic information of the word; (2) they model user/item attributes in a static and independent way, ignoring the potential feature interaction between them. Therefore, we propose a novel Review-aware Interactive Graph Convolutional Network (RI-GCN) for review-based item recommendation. Specifically, we design a Review-aware GCN component to model the message propagation of graphs constructed from reviews, capturing the contextual features of words. A feature interactive GCN component is then proposed to capture the user/item high-order collaborative features in the user-item graph, enabling the model to further complement and refine u ser/item a ttributes. Finally, we adopt a Factorization Machine model for the recommendation task. Experimental results demonstrate that the proposed model is superior to state-of-the-art models. © 2022 IEEE.
Keyword ：
Backpropagation Backpropagation Convolution Convolution Convolutional neural networks Convolutional neural networks Graph neural networks Graph neural networks Recommender systems Recommender systems Semantics Semantics
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| GB/T 7714 | Cai, Yijin , Wang, Yilei , Wang, Weijin et al. RI-GCN: Review-aware Interactive Graph Convolutional Network for Review-based Item Recommendation [C] . 2022 : 475-484 . |
| MLA | Cai, Yijin et al. "RI-GCN: Review-aware Interactive Graph Convolutional Network for Review-based Item Recommendation" . (2022) : 475-484 . |
| APA | Cai, Yijin , Wang, Yilei , Wang, Weijin , Chen, Wenting . RI-GCN: Review-aware Interactive Graph Convolutional Network for Review-based Item Recommendation . (2022) : 475-484 . |
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Chinese medical named entity recognition (NER) task usually lacks sufficient annotation data, and it contains many medical professional terms and abbreviations, making the NER task more difficult. In addition, compared with English NER, Chinese NER is more challenging because it lacks standard feature symbols to determine named entity boundaries. Therefore, Chinese NER needs to perform word segmentation. In this paper, we are inspired by lexicon-based BERT and propose a novel method for Chinese medical NER task. Besides, We design a template-based strategy to enrich the words' information and improve the model's ability to distinguish medical professional terms and abbreviations. Our method enhances the word segmentation accuracy by introducing the external medical lexicon. To verify the effectiveness of our method, we carry out experiments on three medical datasets and our method improves them by 0.92%, 1.18% and 1.55% F1-score compared to baseline.
Keyword ：
Chinese medical NER Chinese medical NER External knowledge External knowledge Prompt Prompt
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| GB/T 7714 | Zhang, Lin , Lai, Peichao , Ye, Feiyang et al. Chinese Medical Named Entity Recognition Using External Knowledge [J]. | PRICAI 2022: TRENDS IN ARTIFICIAL INTELLIGENCE, PT II , 2022 , 13630 : 359-371 . |
| MLA | Zhang, Lin et al. "Chinese Medical Named Entity Recognition Using External Knowledge" . | PRICAI 2022: TRENDS IN ARTIFICIAL INTELLIGENCE, PT II 13630 (2022) : 359-371 . |
| APA | Zhang, Lin , Lai, Peichao , Ye, Feiyang , Fang, Ruixiong , Wang, Ruiqing , Li, Jiayong et al. Chinese Medical Named Entity Recognition Using External Knowledge . | PRICAI 2022: TRENDS IN ARTIFICIAL INTELLIGENCE, PT II , 2022 , 13630 , 359-371 . |
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