2020 6th International Conference on Web Research (ICWR)最新文献_第6页

Bidirectional LSTM Deep Model for Online Doctor Reviews Polarity Detection 在线医生评论极性检测的双向LSTM深度模型

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122289

Mohammad Ehsan Basiri, R. Chegeni, Aria Naseri Karimvand, Shahla Nemati

{"title":"Bidirectional LSTM Deep Model for Online Doctor Reviews Polarity Detection","authors":"Mohammad Ehsan Basiri, R. Chegeni, Aria Naseri Karimvand, Shahla Nemati","doi":"10.1109/ICWR49608.2020.9122289","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122289","url":null,"abstract":"Online medical reviews contain patients' subjective evaluations and reflect their satisfaction with the treatment process and doctors. Mining and analysis of sentiment expressed in these medical data may be vital for different applications including adverse drug effects detection, doctor recommendation, and healthcare quality assessment. Nevertheless, medical sentiment analysis is a challenging and complex task because patients who write the reviews are usually non-professional users and tend to use informal language. The problem is more challenging in the Persian language due to its resource scarcity and complex structure. In this study, we introduce PODOR, a Persian dataset of online doctor reviews extracted from social web. Also, we propose a deep model based on the bidirectional long short-term memory for polarity detection of PODOR reviews. To show the effectiveness and suitability of the proposed model, we compared the model with six traditional supervised machine learning methods and three deep models. Preliminary comparative results indicated that our model outperformed traditional methods by 8% and 7%, and deep models by 2% and 3% in terms of accuracy and f1-measure.","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127237797","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: 7

A Multiagent Approach To Web Service Composition Based On TROPOS Methodology 基于TROPOS方法的Web服务组合多代理方法

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-04-01 DOI: 10.1109/ICWR49608.2020.9122315

A. Rostampour, Rouhollah Abolhasani, F. Taghiyareh

引用次数: 0

Analysis of ResearchGate, a Community Detection Approach 社区检测方法——ResearchGate分析

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-03-12 DOI: 10.1109/ICWR49608.2020.9122296

M. Heydari, B. Teimourpour

{"title":"Analysis of ResearchGate, a Community Detection Approach","authors":"M. Heydari, B. Teimourpour","doi":"10.1109/ICWR49608.2020.9122296","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122296","url":null,"abstract":"We are living in the data age. Communications over scientific networks creates new opportunities for researchers who aim to discover the hidden pattern in these huge repositories. This study utilizes network science to create collaboration network of Iranian Scientific Institutions. A modularity-based approach applied to find network communities. To reach a big picture of science production flow, analysis of the collaboration network is crucial. Our results demonstrated that geographic location closeness and ethnic attributes has important roles in academic collaboration network establishment. Besides, it shows that famous scientific centers in the capital city of Iran, Tehran has strong influence on the production flow of scientific activities. These academic papers are mostly viewed and downloaded from the United State of America, China, India, and Iran. The motivation of this research is that by discovering hidden communities in the network and finding the structure of intuitions communications, we can identify each scientific center research potential separately and clear mutual scientific fields. Therefore, an efficient strategic program can be design, develop and test to keep scientific institutions in progress path and navigate their research goals into a straight useful roadmap to identify and fill the unknown gaps.","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123177146","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: 2

Predicting Subjective Features from Questions on QA Websites using BERT 利用BERT预测QA网站上问题的主观特征

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-02-24 DOI: 10.1109/ICWR49608.2020.9122318

Issa Annamoradnejad, MohammadAmin Fazli, J. Habibi

{"title":"Predicting Subjective Features from Questions on QA Websites using BERT","authors":"Issa Annamoradnejad, MohammadAmin Fazli, J. Habibi","doi":"10.1109/ICWR49608.2020.9122318","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122318","url":null,"abstract":"Community Question-Answering websites, such as StackOverflow and Quora, expect users to follow specific guidelines in order to maintain content quality. These systems mainly rely on community reports for assessing contents, which has serious problems, such as the slow handling of violations, the loss of normal and experienced users' time, the low quality of some reports, and discouraging feedback to new users. Therefore, with the overall goal of providing solutions for automating moderation actions in Q&A websites, we aim to provide a model to predict 20 quality or subjective aspects of questions in QA websites. To this end, we used data gathered by the CrowdSource team at Google Research in 2019 and fine-tuned pre-trained BERT model on our problem. Based on our evaluation, model achieved value of 0.046 for Mean-Squared-Error (MSE) after 2 epochs of training, which did not improve substantially in the next ones. Results confirm that by simple fine-tuning, we can achieve accurate models in little time and on less amount of data.11Code is available at: https://github.com/Moradnejad/Predicting-Subjective-Features-on-QA-Websites","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"614 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132530152","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: 12

Making AdaBoost Less Prone to Overfitting on Noisy Datasets 使AdaBoost不容易在有噪声的数据集上过度拟合

2020 6th International Conference on Web Research (ICWR) Pub Date : 2020-02-21 DOI: 10.1109/ICWR49608.2020.9122292

Zainab Ghadiri Modarres, M. Shabankhah, A. Kamandi

{"title":"Making AdaBoost Less Prone to Overfitting on Noisy Datasets","authors":"Zainab Ghadiri Modarres, M. Shabankhah, A. Kamandi","doi":"10.1109/ICWR49608.2020.9122292","DOIUrl":"https://doi.org/10.1109/ICWR49608.2020.9122292","url":null,"abstract":"AdaBoost is perhaps one of the most well-known ensemble learning algorithms. In simple terms, the idea in AdaBoost is to train a number of weak learners in an increamental fashion where each new learner tries to focus more on those samples that were misclassfied by the preceding classifiers. Consequently, in the presence of noisy data samples, the new leraners will somehow memorize the data, which in turn will lead to an overfitted model. The main objective of this paper is to provide a generalized version of the AdaBoost algorithm that avoids overfitting, and performs better when the data samples are corrupted with noise. To this end, we make use of another ensemble learning algorithm called ValidBoost [15], and introduce a mechanism to dynamically determine the thresholds for both the error rate of each classifier and the error rate in each iteration. These threshholds enable us to control the error rate of the algorithm. Experimental simulations have been made on several benchmark datasets including Web datasets such as “Website Phishing Data Set” and “Page Blocks Classification Data Set” to evaluate the performance of our proposed algorithm.","PeriodicalId":231982,"journal":{"name":"2020 6th International Conference on Web Research (ICWR)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134140336","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