Information Processing & Management最新文献

{"title":"A diversity-enhanced knowledge distillation model for practical math word problem solving","authors":"Yi Zhang ,&nbsp;Guangyou Zhou ,&nbsp;Zhiwen Xie ,&nbsp;Jinjin Ma ,&nbsp;Jimmy Xiangji Huang","doi":"10.1016/j.ipm.2025.104059","DOIUrl":"10.1016/j.ipm.2025.104059","url":null,"abstract":"<div><div>Math Word Problem (MWP) solving is a critical task in natural language processing, has garnered significant research interest in recent years. Various recent studies heavily rely on Seq2Seq models and their extensions (e.g., Seq2Tree and Graph2Tree) to generate mathematical equations. While effective, these models struggle to generate diverse but counterpart solution equations, limiting their generalization across various math problem scenarios. In this paper, we introduce a novel Diversity-enhanced Knowledge Distillation (DivKD) model for practical MWP solving. Our approach proposes an adaptive diversity distillation method, in which a student model learns diverse equations by selectively transferring high-quality knowledge from a teacher model. Additionally, we design a diversity prior-enhanced student model to better capture the diversity distribution of equations by incorporating a conditional variational auto-encoder. Extensive experiments on four MWP benchmark datasets demonstrate that our approach achieves higher answer accuracy than strong baselines while maintaining high efficiency for practical applications.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 3","pages":"Article 104059"},"PeriodicalIF":7.4,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Asymmetric Supervised Cross-Modal Hashing with consensus matrix","authors":"Yinan Li ,&nbsp;Jun Long ,&nbsp;Youyuan Huang ,&nbsp;Zhan Yang","doi":"10.1016/j.ipm.2024.104037","DOIUrl":"10.1016/j.ipm.2024.104037","url":null,"abstract":"<div><div>Supervised hashing has garnered considerable attention in cross-modal retrieval by programming annotated diverse modality data into the unified binary representation that facilitates efficient retrieval and lightweight storage. Despite its advantages, a major challenge remains, how to get the utmost out of annotated information and derive robust common representation that accurately preserves the intrinsic relations across heterogeneous modalities. In this paper, we present an innovative <strong>A</strong>daptive <strong>A</strong>symmetric <strong>S</strong>upervised <strong>C</strong>ross-modal <strong>H</strong>ashing method with consensus matrix to tackle the problem. We begin by formulating the proposition through matrix factorization to obtain the common representation utilizing consensus matrix efficiently. To safeguard the completeness of diverse modality data, we incorporate them via adaptive weight factors along with nuclear norms. Furthermore, an asymmetric hash learning framework between the representative coefficient matrices that come from common representation and semantic labels was constructed to constitute concentrated hash codes. Additionally, a valid discrete optimization algorithm was programmed. Comprehensive experiments conducted on MIRFlirck, NUS-WIDE, and IARP-TC12 datasets validate that <strong>A2SCH</strong> outperforms leading-edge hashing methods in cross-modal retrieval tasks.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 3","pages":"Article 104037"},"PeriodicalIF":7.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contrastive deep graph clustering with hard boundary sample awareness","authors":"Linlin Zhu,&nbsp;Heli Sun,&nbsp;Xiaoyong Huang,&nbsp;Pan Lou,&nbsp;Liang He","doi":"10.1016/j.ipm.2024.104050","DOIUrl":"10.1016/j.ipm.2024.104050","url":null,"abstract":"<div><div>Contrastive deep graph clustering is a graph data clustering method that combines deep learning and contrastive learning, aiming to realize accurate clustering of graph nodes. Although existing hard sample mining-based methods show positive results, they face the following challenges: (1) Clustering methods based on data similarity may lose important intrinsic groupings and association patterns, thereby affecting a comprehensive understanding and interpretation of the data. (2) In the measurement of hard samples, ignoring hard boundary samples may exacerbate clustering bias. To address these issues, we propose a new contrastive deep graph clustering method called Hard Boundary Sample Aware Network (HBSAN), which introduces attribute and structure enhanced encoding and generalized dynamic hard boundary sample weighting modulation strategy. Specifically, we optimize the similarity computation among samples through adaptive attribute embedding and multiview structure embedding techniques to deeply explore the intrinsic connections among samples, thereby aiding in the measurement of hard boundary samples. Furthermore, we leverage the unreliable confidence information obtained from initial clustering analysis to design an innovative hard boundary sample weight modulation function. This function first identifies the hard boundary samples and then dynamically reduces their weights, effectively enhancing the discriminative capability of network in ambiguous classification scenarios. Combining extensive experimental evaluations and in-depth analysis, our approach achieves state-of-the-art performance and establishes superior results in handling complex network clustering tasks.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 3","pages":"Article 104050"},"PeriodicalIF":7.4,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drivers of consumer group participation in an online shopping event: Alibaba's singles’ day","authors":"Jen-Her Wu ,&nbsp;Qi Li ,&nbsp;Lifang Peng ,&nbsp;Simon Robinson ,&nbsp;Yi-Cheng Chen","doi":"10.1016/j.ipm.2025.104067","DOIUrl":"10.1016/j.ipm.2025.104067","url":null,"abstract":"<div><div>Singles’ Day, an online shopping event (OSE), is a dynamic digital experience with consumer incentives, hedonic presence, and social gamification designed to encourage active social interaction. Based upon Activity theory, we identified intrinsic motivation, platform synergy, hedonic presence, and social gamification as key factors affecting consumer group participation during an OSE. Using 614 valid responses collected from an OSE, we reveal: (1) intrinsic motivation, platform synergy, and hedonic presence had direct effects on social gamification, which in turn affected consumer group participation, and (2) social gamification exerted significant mediating impacts on the influencing relationships between intrinsic motivation, platform synergy, hedonic presence, and group participation. These findings provide a nuanced understanding of the interplay between tools and group behavioural outcomes in OSEs. This study contributes a crucial extension of the in-depth understanding of the mechanisms by which elements of Activity theory and IT-enabled platforms individually and jointly enhance consumer group participation.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 3","pages":"Article 104067"},"PeriodicalIF":7.4,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A contracted container-based code component collaboration model with reusable but invisible right management","authors":"Wei Wang ,&nbsp;Zhenping Xie","doi":"10.1016/j.ipm.2024.104057","DOIUrl":"10.1016/j.ipm.2024.104057","url":null,"abstract":"<div><div>Existing methodologies for the code collaboration are facing challenges such as leakage of privacy data and insecure centralized management. To alleviate these challenges, we propose a contracted container-based code component collaboration model. Firstly, a specific blockchain is developed as a computational network that supports the storage and verification of sensitive data, which integrates an off-chain file system and encryption algorithms to realize the traceability and version control of privacy data. Secondly, the containerization and smart contract technology are introduced to enhance access control and the secure management and collaboration of code components. Thirdly, a protocol is designed to ensure the high reliability of cross-node information communication. Finally, the results of the prototype experiment demonstrate that the model effectively resists common attacks, meets critical security criteria, and maintains stable performance in the collaboration process. Moreover, compared to the state-of-the-art research, our model implements more valuable functionality characteristics, design goals and security attributes in the code component collaboration practice.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 3","pages":"Article 104057"},"PeriodicalIF":7.4,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"INSNER: A generative instruction-based prompting method for boosting performance in few-shot NER","authors":"Peiwen Zhao ,&nbsp;Chong Feng ,&nbsp;Peiguang Li ,&nbsp;Guanting Dong ,&nbsp;Sirui Wang","doi":"10.1016/j.ipm.2024.104040","DOIUrl":"10.1016/j.ipm.2024.104040","url":null,"abstract":"<div><div>Most existing Named Entity Recognition (NER) methods require a large scale of labeled data and exhibit poor performance in low-resource scenarios. Thus in this paper, we propose INSNER, a generative INStruction-based prompting method for few-shot NER. Specifically, we introduce a unified instruction to guide the model in extracting correct entities in response to the instruction, and construct synthetic verbalizers, which support complex types, to encourage effective knowledge transfer. We organize the NER results in natural language form, which mitigates the gap between pre-training and fine-tuning of language models. Furthermore, to facilitate the model to learn task-related knowledge and rich label semantics, we introduce entity-oriented prompt-tuning as an auxiliary task. We conduct in-domain and cross-domain experiments in few-shot settings on 4 datasets, and extensive analyses to validate the effectiveness and generalization ability of INSNER. Experimental results demonstrate that INSNER significantly outperforms current methods in few-shot settings, especially huge improvements(+12.0% F1) over the powerful ChatGPT in MIT Movie Complex both under a 10-shot setting.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 3","pages":"Article 104040"},"PeriodicalIF":7.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DuST: Chinese NER using dual-grained syntax-aware transformer network","authors":"Yinlong Xiao ,&nbsp;Zongcheng Ji ,&nbsp;Jianqiang Li ,&nbsp;Mei Han","doi":"10.1016/j.ipm.2024.104041","DOIUrl":"10.1016/j.ipm.2024.104041","url":null,"abstract":"<div><div>Recent studies have attempted to exploit syntactic information (<em>e.g.,</em> dependency relation) to enhance Chinese named entity recognition (NER) performance and achieved promising results. These methods usually leverage single-grained syntactic parsing results which are based on single-grained word segmentation. However, entities may be annotated with varying granularities, resulting in inconsistent boundaries when compared to single-grained results. Therefore, merely using single-grained syntactic information may inadvertently introduce noise into boundary detection in Chinese NER. In this paper, we introduce a Dual-grained Syntax-aware Transformer network (DuST) to mitigate the noise introduced by single-grained syntactic parsing results. We first introduce coarse- and fine-grained syntactic dependency parsing results to comprehensively consider possible boundary scenarios. We then design the DuST network with dual syntax-aware Transformers to capture syntax-enhanced features at different granularities, a contextual Transformer to model the contextual features and an aggregation module to dynamically aggregate these features. Experiments are conducted on four widely-used Chinese NER datasets and our model achieves superior performance. Specifically, our approach outperforms two single-grained syntax-enhanced baselines with an increase of up to 3.9% and 2.94% in F1 score, respectively.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 3","pages":"Article 104041"},"PeriodicalIF":7.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive-solver framework for dynamic strategy selection in large language model reasoning","authors":"Jianpeng Zhou ,&nbsp;Wanjun Zhong ,&nbsp;Yanlin Wang ,&nbsp;Jiahai Wang","doi":"10.1016/j.ipm.2024.104052","DOIUrl":"10.1016/j.ipm.2024.104052","url":null,"abstract":"<div><div>Large Language Models (LLMs) demonstrate impressive ability in handling reasoning tasks. However, unlike humans who can instinctively adapt their problem-solving strategies to the complexity of task, most LLM-based methods adopt a one-size-fits-all approach. These methods employ consistent models, sample sizes, prompting methods and levels of problem decomposition, regardless of the problem complexity. The inflexibility of these methods can bring unnecessary computational overhead or sub-optimal performance. To address this limitation, we introduce an Adaptive-Solver (AS) framework that dynamically adapts solving strategies to suit various problems, enabling the flexible allocation of test-time computational resources. The framework functions with two primary modules. The initial <em>evaluation</em> module assesses the reliability of the current solution using answer consistency. If the solution is deemed unreliable, the subsequent <em>adaptation</em> module comes into play. Within this module, various types of adaptation strategies are employed collaboratively. Through such dynamic and multi-faceted adaptations, our framework can help reduce computational consumption and improve performance. Experimental results from complex reasoning benchmarks reveal that our method can significantly reduce API costs (up to 85%) while maintaining original performance. Alternatively, it achieves up to 4.5% higher accuracy compared to the baselines at the same cost. The datasets and code are available at <span><span>https://github.com/john1226966735/Adaptive-Solver</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 3","pages":"Article 104052"},"PeriodicalIF":7.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DocExtractNet: A novel framework for enhanced information extraction from business documents","authors":"Zhengjin Yan ,&nbsp;Zheng Ye ,&nbsp;Jun Ge ,&nbsp;Jun Qin ,&nbsp;Jing Liu ,&nbsp;Yu Cheng ,&nbsp;Cathal Gurrin","doi":"10.1016/j.ipm.2024.104046","DOIUrl":"10.1016/j.ipm.2024.104046","url":null,"abstract":"<div><div>Efficient extraction of critical information from receipt is essential for automating financial processes and supporting timely decision-making in businesses. However, this process faces significant challenges, starting with variations in the quality of scanned receipt images due to differences in scanning equipment, followed by the complexity of diverse receipt formats, and further complicated by handwritten elements and noise, making accurate extraction particularly difficult. Therefore, to address these issues, we propose a model framework called DocExtractNet, based on LayoutLMv3, designed for extracting key information from receipt. Firstly, we introduce the ImageEnhance method to process image modality features, enhancing image clarity and significantly improving recognition accuracy for low-quality images. Then, we implement the PrecisionHints strategy to supplement missing key–value pairs in the text modality, improving data integrity and the model’s overall performance. Furthermore, we apply the CrossModalFusion method to combine both image and text features, allowing the model to better understand and extract receipt information. The experimental results on the Finance-Receipts, FUNSD, and CORD datasets show that DocExtractNet significantly improves F1 scores compared to other models, with F1 scores reaching 97.07% for Finance-Receipts, 91.80% for FUNSD, and 97.38% for CORD, highlighting its superior performance in receipt information extraction.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 3","pages":"Article 104046"},"PeriodicalIF":7.4,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A semantic framework for enhancing pseudo-relevance feedback with soft negative sampling and contrastive learning","authors":"Min Pan ,&nbsp;Shuting Zhou ,&nbsp;Jinguang Chen ,&nbsp;Ellen Anne Huang ,&nbsp;Jimmy X. Huang","doi":"10.1016/j.ipm.2024.104058","DOIUrl":"10.1016/j.ipm.2024.104058","url":null,"abstract":"<div><div>In the field of information Retrieval (IR), Pseudo-relevance feedback (PRF) and Query Expansion (QE) techniques have garnered significant attention for their efficacy in enhancing retrieval effectiveness. However, traditional PRF approaches predominantly concentrate solely on pseudo-relevant documents identified during the initial retrieval stage, neglecting the rich semantic information embedded within non-pseudo-relevant documents. This paper introduces an innovative PRF model that integrates soft negative samples and contrastive learning to address this limitation, aiming for a more comprehensive capture and representation of semantics. First, we employ the BM25 algorithm as the baseline retrieval mechanism to accurately pinpoint pseudo-relevant documents from the first stage retrieval and assign weights to their terms. Second, a contrastive learning strategy is introduced to distill semantic features from all documents globally, further refining the semantic weights of terms. To mitigate the risk of information loss associated with soft negative samples, we ingeniously leverage the statistical properties of kernel function to precisely gauge the co-occurrence frequencies between terms, ensuring the preservation of core information and thus obtaining kernel function term co-occurrence weights. Third, we select semantically related terms highly relevant to the query for creating an optimized query by balancing these three weight distributions. Extensive empirical analyses conducted on several TREC datasets demonstrate the practical feasibility of our proposed model. It outperforms baseline models and state-of-the-art technologies on core evaluation metrics such as MAP, P@10, NDCG, and MRR. Deeper comparative experiments and case studies reveal that the expansion terms generated by our model exhibits a deeper level of semantic coherence with the original query, underscoring the dual advantages of the model in both theory and practice. In summary, the model presented herein not only opens a new path at the technical level, but also provides a more accurate and efficient solution for real-world applications in IR.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":"62 3","pages":"Article 104058"},"PeriodicalIF":7.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143139420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}