Knowledge and Information Systems最新文献_第2页

Comprehensive review and comparative analysis of transformer models in sentiment analysis 情感分析中变压器模型的全面回顾和比较分析

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-09-06 DOI: 10.1007/s10115-024-02214-3

Hadis Bashiri, Hassan Naderi

{"title":"Comprehensive review and comparative analysis of transformer models in sentiment analysis","authors":"Hadis Bashiri, Hassan Naderi","doi":"10.1007/s10115-024-02214-3","DOIUrl":"https://doi.org/10.1007/s10115-024-02214-3","url":null,"abstract":"Sentiment analysis has become an important task in natural language processing because it is used in many different areas. This paper gives a detailed review of sentiment analysis, including its definition, challenges, and uses. Different approaches to sentiment analysis are discussed, focusing on how they have changed and their limitations. Special attention is given to recent improvements with transformer models and transfer learning. Detailed reviews of well-known transformer models like BERT, RoBERTa, XLNet, ELECTRA, DistilBERT, ALBERT, T5, and GPT are provided, looking at their structures and roles in sentiment analysis. In the experimental section, the performance of these eight transformer models is compared across 22 different datasets. The results show that the T5 model consistently performs the best on multiple datasets, demonstrating its flexibility and ability to generalize. XLNet performs very well in understanding irony and sentiments related to products, while ELECTRA and RoBERTa perform best on certain datasets, showing their strengths in specific areas. BERT and DistilBERT often perform the lowest, indicating that they may struggle with complex sentiment tasks despite being computationally efficient.","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"11 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Sarcasm detection using optimized bi-directional long short-term memory 利用优化的双向长短期记忆进行讽刺检测

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-09-06 DOI: 10.1007/s10115-024-02210-7

Vidyullatha Sukhavasi, Venkatrama Phani kumar Sistla, Venkatesulu Dondeti

{"title":"Sarcasm detection using optimized bi-directional long short-term memory","authors":"Vidyullatha Sukhavasi, Venkatrama Phani kumar Sistla, Venkatesulu Dondeti","doi":"10.1007/s10115-024-02210-7","DOIUrl":"https://doi.org/10.1007/s10115-024-02210-7","url":null,"abstract":"In the current era, the number of social network users continues to increase day by day due to the vast usage of interactive social networking sites like Twitter, Facebook, Instagram, etc. On these sites, users generate posts, whereas the attitude of followers towards factor utilization like situation, sound, feeling, and so on can be analysed. But most people feel difficult to analyse feelings accurately, which is one of the most difficult problems in natural language processing. Some people expose their opinions with different sole meanings, and this sophisticated form of expressing sentiments through irony or mockery is termed sarcasm. The sarcastic comments, tweets or feedback can mislead data mining activities and may result in inaccurate predictions. Several existing models are used for sarcasm detection, but they have resulted in inaccuracy issues, huge time consumption, less training ability, high overfitting issues, etc. To overcome these limitations, an effective model is introduced in this research to detect sarcasm. Initially, the data are collected from publicly available sarcasmania and Generic sarcasm-Not sarcasm (Gen-Sarc-Notsarc) datasets. The collected data are pre-processed using stemming and stop word removal procedures. The features are extracted using the inverse filtering (IF) model through hash index creation, keyword matching and ranking. The optimal features are selected using adaptive search and rescue (ASAR) optimization algorithm. To enhance the accuracy of sarcasm detection, an optimized Bi-LSTM-based deep learning model is proposed by integrating Bi-directional long short-term memory (Bi-LSTM) with group teaching optimization (GTO). Also, the LSTM + GTO model is proposed to compare its performance with the Bi-LSTM + GTO model. The proposed models are compared with existing classifier approaches to prove the model’s superiority using PYTHON. The accuracy of 98.24% and 98.36% are attained for sarcasmania and Gen-Sarc-Notsarc datasets.","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"15 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

CAERS-CF: enhancing convolutional autoencoder recommendations through collaborative filtering CAERS-CF：通过协同过滤增强卷积自动编码器的推荐功能

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-09-06 DOI: 10.1007/s10115-024-02204-5

Amirhossein Ghadami, Thomas Tran

{"title":"CAERS-CF: enhancing convolutional autoencoder recommendations through collaborative filtering","authors":"Amirhossein Ghadami, Thomas Tran","doi":"10.1007/s10115-024-02204-5","DOIUrl":"https://doi.org/10.1007/s10115-024-02204-5","url":null,"abstract":"Recommendation systems are crucial in boosting companies’ revenues by implementing various strategies to engage customers and encourage them to invest in products or services. Businesses constantly desire to enhance these systems through different approaches. One effective method involves using hybrid recommendation systems, known for their ability to create high-performance models. We introduce a hybrid recommendation system that leverages two types of recommendation systems: first, a novel deep learning-based recommendation system that utilizes users’ and items’ content data, and second, a traditional recommendation system that employs users’ past behaviour data. We introduce a novel deep learning-based recommendation system called convolutional autoencoder recommendation system (CAERS). It uses a convolutional autoencoder (CAE) to capture high-order meaningful relationships between users’ and items’ content information and decode them to predict ratings. Subsequently, we design a traditional model-based collaborative filtering recommendation system (CF) that leverages users’ past behaviour data, utilizing singular value decomposition (SVD). Finally, in the last step, we combine the two method’s predictions with linear regression. We determine the optimal weight for each prediction generated by the collaborative filtering and the deep learning-based recommendation system. Our main objective is to introduce a hybrid model called CAERS-CF that leverages the strengths of the two mentioned approaches. For experimental purposes, we utilize two movie datasets to showcase the performance of CAERS-CF. Our model outperforms each constituent model individually and other state-of-the-art deep learning or hybrid models. Across both datasets, the hybrid CAERS-CF model demonstrates an average RMSE improvement of approximately 3.70% and an average MAE improvement of approximately 5.96% compared to the next best models.","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"15 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Targeted training for numerical reasoning with large language models 利用大型语言模型进行有针对性的数字推理训练

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-09-06 DOI: 10.1007/s10115-024-02216-1

Xiao Li, Sichen Liu, Yin Zhu, Gong Cheng

引用次数: 0

Generative user-experience research for developing domain-specific natural language processing applications 开发特定领域自然语言处理应用程序的用户体验生成研究

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-09-04 DOI: 10.1007/s10115-024-02212-5

Anastasia Zhukova, Lukas von Sperl, Christian E. Matt, Bela Gipp

{"title":"Generative user-experience research for developing domain-specific natural language processing applications","authors":"Anastasia Zhukova, Lukas von Sperl, Christian E. Matt, Bela Gipp","doi":"10.1007/s10115-024-02212-5","DOIUrl":"https://doi.org/10.1007/s10115-024-02212-5","url":null,"abstract":"User experience (UX) is a part of human–computer interaction research and focuses on increasing intuitiveness, transparency, simplicity, and trust for the system users. Most UX research for machine learning or natural language processing (NLP) focuses on a data-driven methodology. It engages domain users mainly for usability evaluation. Moreover, more typical UX methods tailor the systems toward user usability, unlike learning about the user needs first. This paper proposes a new methodology for integrating generative UX research into developing domain NLP applications. Generative UX research employs domain users at the initial stages of prototype development, i.e., ideation and concept evaluation, and the last stage for evaluating system usefulness and user utility. The methodology emerged from and is evaluated on a case study about the full-cycle prototype development of a domain-specific semantic search for daily operations in the process industry. A key finding of our case study is that involving domain experts increases their interest and trust in the final NLP application. The combined UX+NLP research of the proposed method efficiently considers data- and user-driven opportunities and constraints, which can be crucial for developing NLP applications.","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"38 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Deep ensembled multi-criteria recommendation system for enhancing and personalizing the user experience on e-commerce platforms 用于增强和个性化电子商务平台用户体验的深度集合多标准推荐系统

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-09-04 DOI: 10.1007/s10115-024-02187-3

Rahul Shrivastava, Dilip Singh Sisodia, Naresh Kumar Nagwani

{"title":"Deep ensembled multi-criteria recommendation system for enhancing and personalizing the user experience on e-commerce platforms","authors":"Rahul Shrivastava, Dilip Singh Sisodia, Naresh Kumar Nagwani","doi":"10.1007/s10115-024-02187-3","DOIUrl":"https://doi.org/10.1007/s10115-024-02187-3","url":null,"abstract":"The commercially applicable Recommendation system (RS) exploits multi-criteria rating-based user-item interaction to learn and personalize user preferences using the Multi-criteria recommendation system (MCRS). The existing MCRS techniques have exploited similarity or aggregation function-based modeling to improve prediction accuracy. However, these MCRS methods do not investigate item aspects-based latent user preferences and criteria-based user-item implicit relationships. Also, the prediction reliability is uncertain due to highly sparse user-item interactions and ignoring auxiliary information support. Hence, this study proposes an ensembled approach that jointly develops the Similarity and aggregation function-based MCRS model (SimAgg-MCRS) and aggregates their user-item predicted preferences into a cumulative preference matrix to generate the final recommendation. First, the proposed model develops the deep neural network (DNN)-based model to aggregate the criteria-based similarity and predicts the overall rating using the aggregated similarity by merging user and item-based predictions. Second, the preference relation-based aggregation function approach develops deep autoencoder-based modeling to exploit the latent relationship among criteria to obtain users’ overall preference over an item by aggregating criteria-wise preference. Finally, the third phase develops the DNN-based ensemble model to integrate the preference matrix of similarity and aggregation function approach to obtain the overall aggregated matrix for the recommendation. The proposed SimAgg-MCRS integrates user and item side information to learn user preferences better. Experimental and prediction accuracy-based comparative evaluation results across Yahoo! Movies and Trip Advisor multi-criteria datasets validate the proposed models’ performance over the baseline MCRS methods.","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"62 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Multi-perspective patient representation learning for disease prediction on electronic health records 通过多视角患者表征学习在电子健康记录上进行疾病预测

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-09-04 DOI: 10.1007/s10115-024-02188-2

Ziyue Yu, Jiayi Wang, Wuman Luo, Rita Tse, Giovanni Pau

{"title":"Multi-perspective patient representation learning for disease prediction on electronic health records","authors":"Ziyue Yu, Jiayi Wang, Wuman Luo, Rita Tse, Giovanni Pau","doi":"10.1007/s10115-024-02188-2","DOIUrl":"https://doi.org/10.1007/s10115-024-02188-2","url":null,"abstract":"Patient representation learning based on electronic health records (EHR) is a critical task for disease prediction. This task aims to effectively extract useful information on dynamic features. Although various existing works have achieved remarkable progress, the model performance can be further improved by fully extracting the trends, variations, and the correlation between the trends and variations in dynamic features. In addition, sparse visit records limit the performance of deep learning models. To address these issues, we propose the multi-perspective patient representation Extractor (MPRE) for disease prediction. Specifically, we propose frequency transformation module (FTM) to extract the trend and variation information of dynamic features in the time–frequency domain, which can enhance the feature representation. In the 2D multi-extraction network (2D MEN), we form the 2D temporal tensor based on trend and variation. Then, the correlations between trend and variation are captured by the proposed dilated operation. Moreover, we propose the first-order difference attention mechanism (FODAM) to calculate the contributions of differences in adjacent variations to the disease diagnosis adaptively. To evaluate the performance of MPRE and baseline methods, we conduct extensive experiments on two real-world public datasets. The experiment results show that MPRE outperforms state-of-the-art baseline methods in terms of AUROC and AUPRC.","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"7 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Lazy learning and sparsity handling in recommendation systems 推荐系统中的懒惰学习和稀疏性处理

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-09-02 DOI: 10.1007/s10115-024-02218-z

Suryanshi Mishra, Tinku Singh, Manish Kumar, Satakshi

{"title":"Lazy learning and sparsity handling in recommendation systems","authors":"Suryanshi Mishra, Tinku Singh, Manish Kumar, Satakshi","doi":"10.1007/s10115-024-02218-z","DOIUrl":"https://doi.org/10.1007/s10115-024-02218-z","url":null,"abstract":"Recommendation systems are ubiquitous in various domains, facilitating users in finding relevant items according to their preferences. Identifying pertinent items that meet their preferences enables users to target the right items. To predict ratings for more accurate forecasts, recommender systems often use collaborative filtering (CF) approaches to sparse user-rated item matrices. Due to a lack of knowledge regarding newly formed entities, the data sparsity of the user-rated item matrix has an enormous effect on collaborative filtering algorithms, which frequently face lazy learning issues. Real-world datasets with exponentially increasing users and reviews make this situation worse. Matrix factorization (MF) stands out as a key strategy in recommender systems, especially for CF tasks. This paper presents a neural network matrix factorization (NNMF) model through machine learning to overcome data sparsity challenges. This approach aims to enhance recommendation quality while mitigating the impact of data sparsity, a common issue in CF algorithms. A thorough comparative analysis was conducted on the well-known MovieLens dataset, spanning from 1.6 to 9.6 M records. The outcomes consistently favored the NNMF algorithm, showcasing superior performance compared to the state-of-the-art method in this domain in terms of precision, recall, ({mathcal {F}}1_{textrm{score}}), MAE, and RMSE.","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"1 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Truss community search in uncertain graphs 不确定图中的桁架群搜索

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-09-02 DOI: 10.1007/s10115-024-02215-2

Bo Xing, Yuting Tan, Junfeng Zhou, Ming Du

{"title":"Truss community search in uncertain graphs","authors":"Bo Xing, Yuting Tan, Junfeng Zhou, Ming Du","doi":"10.1007/s10115-024-02215-2","DOIUrl":"https://doi.org/10.1007/s10115-024-02215-2","url":null,"abstract":"Given an uncertain graph, community search is used to return dense subgraphs that contain the query vertex and satisfy the probability constraint. With the proliferation of uncertain graphs in practical applications, community search has become increasingly important in practical applications to help users make decisions in advertising recommendations, conference organization, etc. However, existing approaches for community search still suffer from two problems. First, they may return subgraphs that cannot meet users’ expectations on structural cohesiveness, due to the existence of cut-vertices/edges. Second, they use floating-point division to update the probability of each edge during computation, resulting in inaccurate results. In this paper, we study community search on uncertain graphs and propose efficient algorithms to address the above two problems. We first propose a novel community model, namely triangle-connected ((k,gamma ))-truss community, to return communities with enhanced cohesiveness. Then, we propose an online algorithm that uses a batch-recalculation strategy to guarantee the accuracy. To improve the performance of community search, we propose an index-based approach. This index organizes all the triangle-connected ((k,gamma ))-truss communities using a forest structure and maintains the mapping relationship from vertices in the uncertain graph to communities in the index. Based on this index, we can get the results of community search easily, without the costly operation as the online approach does. Finally, we conduct rich experiments on 10 real-world graphs. The experimental results verified the effectiveness and efficiency of our approaches.","PeriodicalId":54749,"journal":{"name":"Knowledge and Information Systems","volume":"13 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142203924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Evaluation metrics on text summarization: comprehensive survey 文本摘要的评估指标：全面调查

IF 2.7 4区计算机科学

Knowledge and Information Systems Pub Date : 2024-08-31 DOI: 10.1007/s10115-024-02217-0

Ensieh Davoodijam, Mohsen Alambardar Meybodi

引用次数: 0