Cognitive Computation最新文献_第3页

Analyzing Emotional Trends from X Platform Using SenticNet: A Comparative Analysis with Cryptocurrency Price 使用 SenticNet 分析来自 X 平台的情感趋势：与加密货币价格的对比分析

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-08-09 DOI: 10.1007/s12559-024-10335-8

Moein Shahiki Tash, Zahra Ahani, Mohim Tash, Olga Kolesnikova, Grigori Sidorov

引用次数: 0

Internet of Things for Emotion Care: Advances, Applications, and Challenges 情感护理物联网：进展、应用与挑战

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-08-07 DOI: 10.1007/s12559-024-10327-8

Xu Xu, Chong Fu, David Camacho, Jong Hyuk Park, Junxin Chen

引用次数: 0

Explainable AI for Text Classification: Lessons from a Comprehensive Evaluation of Post Hoc Methods 用于文本分类的可解释人工智能：后发方法综合评估的启示

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-08-06 DOI: 10.1007/s12559-024-10325-w

Mirko Cesarini, Lorenzo Malandri, Filippo Pallucchini, Andrea Seveso, Frank Xing

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Cognitive-Inspired Deep Learning Models for Aspect-Based Sentiment Analysis: A Retrospective Overview and Bibliometric Analysis 用于基于方面的情感分析的认知启发深度学习模型：回顾性概述和文献计量分析

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-08-06 DOI: 10.1007/s12559-024-10331-y

Xieling Chen, Haoran Xie, S. Joe Qin, Yaping Chai, Xiaohui Tao, Fu Lee Wang

{"title":"Cognitive-Inspired Deep Learning Models for Aspect-Based Sentiment Analysis: A Retrospective Overview and Bibliometric Analysis","authors":"Xieling Chen, Haoran Xie, S. Joe Qin, Yaping Chai, Xiaohui Tao, Fu Lee Wang","doi":"10.1007/s12559-024-10331-y","DOIUrl":"https://doi.org/10.1007/s12559-024-10331-y","url":null,"abstract":"As cognitive-inspired computation approaches, deep neural networks or deep learning (DL) models have played important roles in allowing machines to reach human-like performances in various complex cognitive tasks such as cognitive computation and sentiment analysis. This paper offers a thorough examination of the rapidly developing topic of DL-assisted aspect-based sentiment analysis (DL-ABSA), focusing on its increasing importance and implications for practice and research advancement. Leveraging bibliometric indicators, social network analysis, and topic modeling techniques, the study investigates four research questions: publication and citation trends, scientific collaborations, major themes and topics, and prospective research directions. The analysis reveals significant growth in DL-ABSA research output and impact, with notable contributions from diverse publication sources, institutions, and countries/regions. Collaborative networks between countries/regions, particularly between the USA and China, underscore global engagement in DL-ABSA research. Major themes such as syntax and structure analysis, neural networks for sequence modeling, and specific aspects and modalities in sentiment analysis emerge from the analysis, guiding future research endeavors. The study identifies prospective avenues for practitioners, emphasizing the strategic importance of syntax analysis, neural network methodologies, and domain-specific applications. Overall, this study contributes to the understanding of DL-ABSA research dynamics, providing a roadmap for practitioners and researchers to navigate the evolving landscape and drive innovations in DL-ABSA methodologies and applications. ","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"56 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141930298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Consensus Model with Non-Cooperative Behavior Adaptive Management Based on Cognitive Psychological State Computation in Large-Scale Group Decision 基于大规模群体决策中认知心理状态计算的非合作行为适应性管理共识模型

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-08-02 DOI: 10.1007/s12559-024-10330-z

Yuetong Chen, Mingrui Zhou, Fengming Liu

{"title":"A Consensus Model with Non-Cooperative Behavior Adaptive Management Based on Cognitive Psychological State Computation in Large-Scale Group Decision","authors":"Yuetong Chen, Mingrui Zhou, Fengming Liu","doi":"10.1007/s12559-024-10330-z","DOIUrl":"https://doi.org/10.1007/s12559-024-10330-z","url":null,"abstract":"Social cognition proposed that individual cognitive psychology was closely related to decision-making behavior. The heterogeneity of individual cognitive psychology has been ignored in large-scale decision-making. This research proposes a novel consensus decision model based on cognitive psychological state computation. Effective trust, cognitive trust, and opinion similarity are integrated to construct a fusion relationship network, and Louvain algorithm is used to divide communities. On this basis, non-cooperative individuals are identified. We quantify and classify individual cognitive psychological states by introducing attitude-belief factors. In this process, the cognitive trust and cognitive expression involved have fuzziness and uncertainty, which are quantified and computed by intuitionistic fuzzy set theory. Considering the difference in cognitive dissonance among non-cooperative individuals with different cognitive states, an adaptive feedback mechanism and trust renewal rule are proposed. The simulation results show that, on the one hand, the consensus model in this paper has a high timeliness. On the other hand, among the four types of cognitive psychological state, the non-cooperative individual with higher attitude factor and lower belief factor had higher management efficiency and consensus-reaching speed.","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"298 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fermatean Fuzzy Dombi Generalized Maclaurin Symmetric Mean Operators for Prioritizing Bulk Material Handling Technologies 用于确定散装物料处理技术优先次序的 Fermatean Fuzzy Dombi 广义 Maclaurin 对称均值算子

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-31 DOI: 10.1007/s12559-024-10323-y

Abhijit Saha, Svetlana Dabic-Miletic, Tapan Senapati, Vladimir Simic, Dragan Pamucar, Ali Ala, Leena Arya

引用次数: 0

Multi-View Cooperative Learning with Invariant Rationale for Document-Level Relation Extraction 利用不变原理进行文档级关系提取的多视图合作学习

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-27 DOI: 10.1007/s12559-024-10322-z

Rui Lin, Jing Fan, Yinglong He, Yehui Yang, Jia Li, Cunhan Guo

{"title":"Multi-View Cooperative Learning with Invariant Rationale for Document-Level Relation Extraction","authors":"Rui Lin, Jing Fan, Yinglong He, Yehui Yang, Jia Li, Cunhan Guo","doi":"10.1007/s12559-024-10322-z","DOIUrl":"https://doi.org/10.1007/s12559-024-10322-z","url":null,"abstract":"Document-level relation extraction (RE) is a complex and significant natural language processing task, as the massive entity pairs exist in the document and are across sentences in reality. However, the existing relation extraction methods (deep learning) often use single-view information (e.g., entity-level or sentence-level) to learn the relational information but ignore the multi-view information, and the explanations of deep learning are difficult to be reflected, although it achieves good results. To extract high-quality relational information from the document and improve the explanations of the model, we propose a multi-view cooperative learning with invariant rationale (MCLIR) framework. Firstly, we design the multi-view cooperative learning to find latent relational information from the various views. Secondly, we utilize invariant rationale to encourage the model to focus on crucial information, which can empower the performance and explanations of the model. We conduct the experiment on two public datasets, and the results of the experiment demonstrate the effectiveness of MCLIR.","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"352 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Disentangling User Cognitive Intent with Causal Reasoning for Knowledge-Enhanced Recommendation 将用户认知意图与因果推理相分离，实现知识增强型推荐

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-18 DOI: 10.1007/s12559-024-10321-0

Hongcai xu, Junpeng Bao, Qika Lin, Lifang Hou, Feng Chen

{"title":"Disentangling User Cognitive Intent with Causal Reasoning for Knowledge-Enhanced Recommendation","authors":"Hongcai xu, Junpeng Bao, Qika Lin, Lifang Hou, Feng Chen","doi":"10.1007/s12559-024-10321-0","DOIUrl":"https://doi.org/10.1007/s12559-024-10321-0","url":null,"abstract":"The primary objective of an effective recommender system is to provide accurate, varied, and personalized recommendations that align with the user’s cognitive intents. Given their ability to represent structural and semantic information effectively, knowledge graphs (KGs) are increasingly being utilized to capture auxiliary information for recommendation systems. This trend is supported by the recent advancements in graph neural network (GNN)-based models for KG-aware recommendations. However, these models often struggle with issues such as insufficient user-item interactions and the misalignment of user intent weights during information propagation. Additionally, they face a popularity bias, which is exacerbated by the disproportionate influence of a small number of highly active users and the limited auxiliary information about items. This bias significantly curtails the effectiveness of the recommendations. To address this issue, we propose a Knowledge-Enhanced User Cognitive Intent Network (KeCAIN), which incorporates item category information to capture user intents with information aggregation and eliminate popularity bias based on causal reasoning in recommendation systems. Experiments on three real-world datasets show that KeCAIN outperforms state-of-the-art baselines.","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"4 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141744867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluative Item-Contrastive Explanations in Rankings 排名中的评价性项目对比解释

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-10 DOI: 10.1007/s12559-024-10311-2

Alessandro Castelnovo, Riccardo Crupi, Nicolò Mombelli, Gabriele Nanino, Daniele Regoli

引用次数: 0

Granular Syntax Processing with Multi-Task and Curriculum Learning 利用多任务和课程学习进行细粒度语法处理

IF 5.4 3区计算机科学

Cognitive Computation Pub Date : 2024-07-08 DOI: 10.1007/s12559-024-10320-1

Xulang Zhang, Rui Mao, Erik Cambria

{"title":"Granular Syntax Processing with Multi-Task and Curriculum Learning","authors":"Xulang Zhang, Rui Mao, Erik Cambria","doi":"10.1007/s12559-024-10320-1","DOIUrl":"https://doi.org/10.1007/s12559-024-10320-1","url":null,"abstract":"Syntactic processing techniques are the foundation of natural language processing (NLP), supporting many downstream NLP tasks. In this paper, we conduct pair-wise multi-task learning (MTL) on syntactic tasks with different granularity, namely Sentence Boundary Detection (SBD), text chunking, and Part-of-Speech (PoS) tagging, so as to investigate the extent to which they complement each other. We propose a novel soft parameter-sharing mechanism to share local and global dependency information that is learned from both target tasks. We also propose a curriculum learning (CL) mechanism to improve MTL with non-parallel labeled data. Using non-parallel labeled data in MTL is a common practice, whereas it has not received enough attention before. For example, our employed PoS tagging data do not have text chunking labels. When learning PoS tagging and text chunking together, the proposed CL mechanism aims to select complementary samples from the two tasks to update the parameters of the MTL model in the same training batch. Such a method yields better performance and learning stability. We conclude that the fine-grained tasks can provide complementary features to coarse-grained ones, while the most coarse-grained task, SBD, provides useful information for the most fine-grained one, PoS tagging. Additionally, the text chunking task achieves state-of-the-art performance when joint learning with PoS tagging. Our analytical experiments also show the effectiveness of the proposed soft parameter-sharing and CL mechanisms.","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":"57 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141572133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0