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The Tourism-Specific Sentiment Vector Construction Based on Kernel Optimization Function

Procedia Computer Science 2017年 122卷 1162-1167页

作者： Luyao Zhu Wei Li Kun Guo Yong Shi Yuanchun Zheng School of Economics and Management University of Chinese Academy of Sciences Beijing China Fictitious Economy & Data Science Research Center Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

Sentiment analysis in tourism domain has drawn much attention in past few years, which calls for more precise sentiment word embedding method. The article proposes a kernel optimization function for sentiment word embedding. And the method aims at integrating the semantic information, statistics information and sentiment information and maintains the similarity between sentiment words in terms of sentiment orientation. The experiment result shows that the optimal sentiment vectors successfully extract the features in terms of sentiment information and the difference between concretization and abstraction of a sentiment words.

关键词： kernel function sentiment vector word embedding sentiment analysis

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An Optimization-Based Truth Discovery Method with Claim Relation

An Optimization-Based Truth Discovery Method with Claim Rela...

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IEEE International Conference on Big knowledge (ICBK)

作者： Jiazhu Xia Ying He Yuxin Jin Xianyu Bao Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

With the advent of the era of big data, the information from multi-sources often conflicts due to that errors and fake information are inevitable. Therefore, how to obtain the most trustworthy or true information (i.e. truth) people need gradually becomes a troublesome problem. In order to meet this challenge, a novel hot technology named truth discovery that can infer the truth and estimate the reliability of the source without supervision has attracted more and more attention. However, most existing truth discovery methods only consider that the information is either same or different rather than the fine-grained relation between them, such as inclusion, support, mutual exclusion, etc. Actually, this situation frequently exists in real-world applications. To tackle the aforementioned issue, we propose a novel truth discovery method named OTDCR in this paper, which can handle the fine-grained relation between the information and infer the truth more effectively through modeling the relation. In addition, a novel method of processing abnormal values is applied to the preprocessing of truth discovery, which is specially designed for categorical data with the relation. Experiments in real dataset show our method is more effective than several outstanding methods.

关键词： Task analysis knowledge engineering Big data Crowdsourcing Reliability engineering Meteorology

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Salient Patch Based NAS for Grading of Colorectal Cancer Histology Images 13

Salient Patch Based NAS for Grading of Colorectal Cancer His...

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13th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2022

作者： Meng, Hang Li, Fei Yang, Zhen Zhu, Qiang Fan, Chaogang Zhan, Shu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Anhui Province Hefei230009 China The Second Affiliated Hospital of Anhui Medical University Anhui Province Hefei230601 China Tongcheng Teachers College Anhui Province Tongcheng231400 China Department of General Surgery The Affiliated BenQ Hospital of Nanjing Medical University Jiangsu Province Nanjing210021 China

ISBN: (纸本)9781950492619

Digital histopathological tissue images are gold material for cancer diagnosis and grades. Convolutional Neural Networks (CNNs) are state-of-the-art models in many image classification tasks. However, considering the limited computing resources, it is not practical to directly train CNNs on gigapixel resolution histopathological tissue images. Therefore, the entire histopathological image is commonly divided into a grid of patches and then fed each patch to CNNs, but not all patches are necessary for specific tasks. Besides, although CNNs are excellent image classifiers, many pre-trained models are based on natural images. Neural Architecture Search (NAS) can realize automatic search, construction, and training specific problemoriented networks. We present salient patch based NAS for grading colorectal cancer (CRC) histology images with these motivations. The proposed framework first applies the classic classification network to extract significant patches from the whole image, then use the network obtained by NAS to grade the patches, and finally aggregates the patch-level predictions to generate the entire CRC histological image label. Our experiments on microscopic colorectal tissue images demonstrate that the proposed method is sufficient to obtain more accurate classification results compared to its counterparts. We also conducted a comprehensive analysis of the different scenarios of the proposed method. © by the International Institute of Informatics and Systemics.

关键词： Grading

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Chinese integral word segmentation recognition based on reliability

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Journal of Information and Computational Science 2009年第1期6卷 533-542页

作者： Wan, Changxuan Wang, Fang School of Information Technology Jiangxi University of Finance and Economics Nanchang 330013 China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang 330013 China School of Humanities Nanchang University Nanchang 330013 China

As one part of preprocessing, automatic word segmentation is an key issue in Chinese information retrieval. Since integral words are put wholly together to compose into the more meaningful words and more express users' query minds in information retrieval, we combine the calculation of mutual information, prefix- and-suffix information of integral word with the reliability of composing into the integral word, and put forward three Chinese integral word segmentation recognition method based on reliability according to the characteristics of the integral words. Then, we design and realize the prototype system of three Chinese integral word segmentation recognition methods based on reliability. Finally, tests and analysis on 2nd SIGHAN (2005) PKU test corpus show that the recognition performance of the system is good, and can satisfy different needs at the same time. © 2009 Binary Information Press.

关键词： Reliability

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Idea-map: A Spatiotemporal View of Research Ideas

Idea-map: A Spatiotemporal View of Research Ideas

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2011 Eighth International Conference on Fuzzy System and knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

作者： HeHu Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering(Renmin University of China) MOE Beijing 100872 P. R. China School of Information Renmin University of China Beijing 100872 P. R. China

in this paper, we describe idea-map, a mash-up application building on top of Linked data Cloud. It reads in user's keywords (about research ideas) and executes a SPARQL query against DBLP endpoint. Spatial and temporal information is extracted and parsed from the query results and is further transformed to SIMILE/EXHIBIT to show a spatiotemporal map for the research ideas. Idea-map shows the feasibility of combing various techniques such as YQL, SIMILE/Exhibit and SPARQL query answering to provide an insightful interface to better understand the interested research ideas.

关键词： Link data Cloud DBLP Spatiotemoral View YQL

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Meta-Path Based Graph Contrastive Learning for Micro-Video Recommendation

SSRN

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SSRN 2022年

作者： He, Ying Wu, Gongqing Cai, Desheng Hu, Xuegang Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230009 China

Nowadays, micro-video sharing platforms have become popular tools for people creating and viewing micro-videos in daily life. Micro-video recommendation task has attracted significant attention from researchers, recently. The key to a high-quality micro-video recommendation system is learning meaningful user and micro-video representations. Recently, many graph neural networks (GNNs) are proposed to learn node representations in graphs, which could be useful for recommendation tasks. However, we argue that directly utilizing existing GNNs in micro-video recommendations is ill-posed. The reasons are: (1) most previous GNNs fail to capture the heterogeneity of heterogeneous graphs since they are designed for homogeneous graphs;(2) they overemphasize node proximity and could hurt the robustness of node embeddings. In contrast to previous work, we propose a meta-path-based graph contrastive learning network (MPGCL) that learns more meaningful user and video embeddings for recommendation. Specifically, we yield homogeneous graphs for user type and video type to better capture heterogeneity based on a well-designed meta-path-based random walk strategy. Furthermore, to learn more robust node embeddings that are less sensitive to noise, we propose a graph contrastive learning network on different types of homogeneous graphs, which maximizes the consistency between graph representations with different views. We do extensive experiments on three real-world datasets and the results show that our model can learn more meaningful embeddings for user and micro-videos and outperforms the strong baselines. © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Research on Crowdsourcing Truth Inference Method Based on Graph Embedding

Research on Crowdsourcing Truth Inference Method Based on Gr...

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IEEE International Conference on Big knowledge (ICBK)

作者： Liangzhu Zhou Xingrui Zhuo Gongqing Wu Zan Zhang Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438599

Crowdsourcing is a cheap and popular method to solve problems that are difficult for computers to handle. Due to the differences in ability among workers on crowdsourcing platforms, existing research use aggregation strategies to deal with the labels of different workers to improve the utility of crowdsourcing data. However, most of these studies are based on probabilistic graphical models, which have problems such as difficulty in setting initial parameters. This paper proposes a novel crowdsourcing method Truth Inference based on Graph Embedding (TIGE) for single-choice questions, the method draws on the idea of graph autoencoder, constructs feature vectors for each crowdsourcing task, embeds the relationship between crowdsourcing tasks and workers in graphs, then uses graph neural networks to convert crowdsourcing problems into graph node prediction problems. The feature vectors are continuously optimized in the convolutional layer to obtain the final result. Compared with the six state-of-the-art algorithms on real-world datasets, our method has significant advantages in accuracy and F1-score.

关键词： Crowdsourcing Training Graphical models Probabilistic logic Prediction algorithms Graph neural networks Robustness

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Ensemble Classification Method Based on Truth Discovery

Ensemble Classification Method Based on Truth Discovery

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IEEE International Conference on Big knowledge (ICBK)

作者： Yuxin Jin Ze Yang Ying He Xianyu Bao Gongqing Wu Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

Classification is a hot topic in such fields as machine learning and data mining. The traditional approach of machine learning is to find a classifier closest to the real classification function, while ensemble classification is to integrate the results of base classifiers, then make an overall prediction. Compared to using a single classifier, ensemble classification can significantly improve the generalization of the learning system in most cases. However, the existing ensemble classification methods rarely consider the weight of the classifier, and there are few methods to consider updating the weights dynamically. In this paper, we are inspired by the idea of truth discovery and propose a new ensemble classification method based on the truth discovery (named ECTD). As far as we know, we are the first to apply the idea of truth discovery in the field of ensemble learning. Experimental results demonstrate that the proposed method performs well in ensemble classification.

关键词： Reliability Machine learning Classification algorithms Task analysis Buildings Computational modeling knowledge engineering

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Gaussian Model-Based Fully Convolutional Networks for Multivariate Time Series Classification

Gaussian Model-Based Fully Convolutional Networks for Multiv...

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IEEE International Conference on Big knowledge (ICBK)

作者： Changyang Tai Ze Yang Huicheng Zhang Gongqing Wu Junwei Lv Xianyu Bao Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Shenzhen Academy of Inspection and Quarantine Shenzhen China

ISBN: (纸本)9781665438599

Multivariate time series (MTS) classification has been regarded as one of the most challenging problems in data mining due to the difficulty in modeling the correlation of variables and samples. In addition, high-dimensional MTS modeling has a large time and space consumption. This paper proposes a novel method, Gaussian Model-based Fully Convolutional Networks (GM-FCN), to improve the performance of high-dimensional MTS classification. Each original MTS is converted into multivariate Gaussian model parameters as the input of FCN. These parameters effectively capture the correlation be-tween MTS variables and significantly reduce the data scale by aligning an MTS size to its dimension. FCN is designed to learn more in-depth features of MTS based on these parameters for modeling the correlation between samples. Thus, GM-FCN can not only model the correlation between variables, but also the correlation between samples. We compare GM-FCN with nine state-of-the-art MTS classification methods, INN-ED, INN-DTW-i, INN-DTW-D, KLD-GMC, MLP, ResNet, Encoder, MCNN, and MCDCNN, on four high-dimensional public datasets, experimen-tal results show that the accuracy of G M - FCN is significantly superior to the others. Besides, the training time of GM-FCN is dozens of times faster than FCN using the original equal-length MTS data as input.

关键词： Training knowledge engineering Deep learning Correlation Conferences Time series analysis data models

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PediaBench: A Comprehensive Chinese Pediatric dataset for Benchmarking Large Language Models

arXiv

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arXiv 2024年

作者： Zhang, Qian Chen, Panfeng Li, Jiali Feng, Linkun Liu, Shuyu Zhao, Heng Chen, Mei Li, Hui Wang, Yanhao State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Guizhou Engineering Laboratory for Advanced Computing and Medical Information Services Guiyang China College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Data Science and Engineering East China Normal University Shanghai China

The emergence of Large Language Models (LLMs) in the medical domain has stressed a compelling need for standard datasets to evaluate their question-answering (QA) performance. Although there have been several benchmark datasets for medical QA, they either cover common knowledge across different departments or are specific to another department rather than pediatrics. Moreover, some of them are limited to objective questions and do not measure the generation capacity of LLMs. Therefore, they cannot comprehensively assess the QA ability of LLMs in pediatrics. To fill this gap, we construct PediaBench, the first Chinese pediatric dataset for LLM evaluation. Specifically, it contains 4,117 objective questions and 1,632 subjective questions spanning 12 pediatric disease groups. It adopts an integrated scoring criterion based on different difficulty levels to thoroughly assess the proficiency of an LLM in instruction following, knowledge understanding, clinical case analysis, etc. Finally, we validate the effectiveness of PediaBench with extensive experiments on 20 open-source and commercial LLMs. Through an in-depth analysis of experimental results, we offer insights into the ability of LLMs to answer pediatric questions in the Chinese context, highlighting their limitations for further improvements. Our code and data are published anonymously at https://***/ACMISLab/PediaBench. © 2024, CC BY-SA.

关键词： Question answering

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