Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00019

Lan Rong, Shumin Wang, He Hu, Zhao Feng, Haiyan Yu, Zhang Lu

{"title":"Center-free intuitionistic fuzzy c-means clustering algorithm based on similarity of hybrid spatial membership for image segmentation","authors":"Lan Rong, Shumin Wang, He Hu, Zhao Feng, Haiyan Yu, Zhang Lu","doi":"10.1109/ICNLP58431.2023.00019","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00019","url":null,"abstract":"In order to address the issue that the center-free fuzzy c-means (CFFCM) clustering algorithm does not consider the texture features and spatial information of pixels, and the time complexity is too high, a center-free intuitionistic fuzzy c-means clustering algorithm based on similarity of hybrid spatial membership for image segmentation is proposed. In the proposed algorithm, the voting model is used to generate intuitionistic fuzzy sets (IFS), and the generated hesitation degree and membership degree are combined with spatial information to design a spatial intuitionistic membership degree similarity model. This model can deal with the similarity between pixels and classes in gray information, so the segmentation efficiency is improved. At the same time, the intuitionistic fuzzy local binary pattern (IFLBP) operator is used to extract the image texture information and introduce it into the objective function. Spatial membership similarity model is used to process texture information and improve the segmentation accuracy of the algorithm. The results of simulation experiment show that the proposed has advantages in both visual effect and evaluation index.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80167774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal Visual Question Answering Model Enhanced with Image Emotional Information 图像情感信息增强的多模态视觉问答模型

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00056

Jin Cai, Guoyong Cai

{"title":"Multimodal Visual Question Answering Model Enhanced with Image Emotional Information","authors":"Jin Cai, Guoyong Cai","doi":"10.1109/ICNLP58431.2023.00056","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00056","url":null,"abstract":"Visual Question Answering is a multimedia understanding task that gives an image and natural language questions related to its content and allows the computer to answer them correctly. The early visual question answering models often ignore the emotional information in the image, resulting in insufficient performance in answering emotional-related questions; on the other hand, the existing visual question answering models that integrate emotional information do not make full use of the key areas of the image and text keywords, and do not understand fine-grained questions deeply enough, resulting in low accuracy. In order to fully integrate image emotional information into the visual question answering model and enhance the ability of the model to answer questions, a multimodal visual question answering model (IEMVQA) enhanced by image emotional information is proposed, and experiments are carried out on the visual question answering benchmark dataset. The final experiment shows that the IEMVQA model performs better than other comparison methods in comprehensive indicators, and verifies the effectiveness of using emotional information to assist visual question answering model.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73803245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks 基于半监督生成对抗网络的孟加拉语虚假评论检测

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00011

Md. Tanvir Rouf Shawon, G. M. Shahariar, F. Shah, Mohammad Shafiul Alam, M. S. Mahbub

{"title":"Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks","authors":"Md. Tanvir Rouf Shawon, G. M. Shahariar, F. Shah, Mohammad Shafiul Alam, M. S. Mahbub","doi":"10.1109/ICNLP58431.2023.00011","DOIUrl":"https://doi.org/10.1109/ICNLP58431.2023.00011","url":null,"abstract":"This paper investigates the potential of semi-supervised Generative Adversarial Networks (GANs) to fine-tune pretrained language models in order to classify Bengali fake reviews from real reviews with a few annotated data. With the rise of social media and e-commerce, the ability to detect fake or deceptive reviews is becoming increasingly important in order to protect consumers from being misled by false information. Any machine learning model will have trouble identifying a fake review, especially for a low resource language like Bengali. We have demonstrated that the proposed semi-supervised GAN-LM architecture (generative adversarial network on top of a pretrained language model) is a viable solution in classifying Bengali fake reviews as the experimental results suggest that even with only 1024 annotated samples, BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and a f1-score of 84.89% outperforming other pretrained language models - BanglaBERT generator, Bangla BERT Base and BanglaElectra by almost 3%, 4% and 10% respectively in terms of accuracy. The experiments were conducted on a manually labeled food review dataset consisting of total 6014 real and fake reviews collected from various social media groups. Researchers that are experiencing difficulty recognizing not just fake reviews but other classification issues owing to a lack of labeled data may find a solution in our proposed methodology.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73889263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

When to Use Large Language Model: Upper Bound Analysis of BM25 Algorithms in Reading Comprehension Task 何时使用大型语言模型:阅读理解任务中BM25算法的上界分析

Icon Pub Date : 2023-03-01 DOI: 10.1109/icnlp58431.2023.00049

Tingzhen Liu, Qianqian Xiong, Shengxi Zhang

引用次数: 1

Computer-aided Analysis of Conceptual Metaphors in English News Report 英语新闻报道中概念隐喻的计算机辅助分析

Icon Pub Date : 2023-03-01 DOI: 10.1109/ICNLP58431.2023.00058

Tu Ying

引用次数: 0

Generalization Algorithm of Multimodal Pre-Training Model Based on Graph-Text Self-Supervised Training 基于图-文本自监督训练的多模态预训练模型概化算法

Icon Pub Date : 2023-02-16 DOI: 10.1109/ICNLP58431.2023.00066

Xiaobing Zhang, Zhenhao Tang, Zi Long, Xianghua Fu

引用次数: 0

Massively Multilingual Language Models for Cross Lingual Fact Extraction from Low Resource Indian Languages 低资源印度语言跨语言事实抽取的大规模多语言模型

Icon Pub Date : 2023-02-09 DOI: 10.48550/arXiv.2302.04790

Bhavyajeet Singh, Pavan Kandru, Anubhav Sharma, Vasudeva Varma

引用次数: 1

There is No Big Brother or Small Brother:Knowledge Infusion in Language Models for Link Prediction and Question Answering 没有老大哥或小弟:链接预测和问答语言模型中的知识注入

Icon Pub Date : 2023-01-10 DOI: 10.48550/arXiv.2301.04013

Ankush Agarwal, Sakharam Gawade, Sachin Channabasavarajendra, P. Bhattacharyya

{"title":"There is No Big Brother or Small Brother:Knowledge Infusion in Language Models for Link Prediction and Question Answering","authors":"Ankush Agarwal, Sakharam Gawade, Sachin Channabasavarajendra, P. Bhattacharyya","doi":"10.48550/arXiv.2301.04013","DOIUrl":"https://doi.org/10.48550/arXiv.2301.04013","url":null,"abstract":"The integration of knowledge graphs with deep learning is thriving in improving the performance of various natural language processing (NLP) tasks. In this paper, we focus on knowledge-infused link prediction and question answering using language models, T5, and BLOOM across three domains:Aviation, Movie, and Web. In this context, we infuse knowledge in large and small language models and study their performance, and find the performance to be similar. For the link prediction task on the Aviation Knowledge Graph, we obtain a 0.2 hits@1 score using T5-small, T5-base, T5-large, and BLOOM. Using template-based scripts, we create a set of 1 million synthetic factoid QA pairs in the aviation domain from National Transportation Safety Board (NTSB) reports. On our curated QA pairs, the three models of T5 achieve a 0.7 hits@1 score. We validate our findings with the paired student t test and Cohen’s kappa scores. For link prediction on Aviation Knowledge Graph using T5-small and T5-large, we obtain a Cohen’s kappa score of 0.76, showing substantial agreement between the models. Thus, we infer that small language models perform similar to large language models with the infusion of knowledge.","PeriodicalId":53637,"journal":{"name":"Icon","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46274359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

ABB-BERT: A BERT model for disambiguating abbreviations and contractions 用于消除缩写和缩写歧义的BERT模型

Icon Pub Date : 2022-07-08 DOI: 10.48550/arXiv.2207.04008

Prateek Kacker, Andi Cupallari, Aswin Giridhar Subramanian, Nimit Jain

引用次数: 0

Towards Multimodal Vision-Language Models Generating Non-Generic Text 生成非通用文本的多模式视觉语言模型

Icon Pub Date : 2022-06-28 DOI: 10.1609/aaai.v36i11.21705

Wes Robbins

引用次数: 0

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