Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics最新文献_第10页

Addressing the cold user problem for model-based recommender systems 解决基于模型的推荐系统的冷用户问题

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106431

Tomas Geurts, F. Frasincar

引用次数: 2

Ontology of human relation extraction based on dependency syntax rules 基于依赖语法规则的人际关系抽取本体

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3109050

Long He, Likun Qiu

引用次数: 1

Augmented SVM with ordinal partitioning for text classification 基于有序划分的增强支持向量机文本分类

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3109428

Yong Shi, Peijia Li, Lingfeng Niu

引用次数: 0

Constructing and visualizing topic forests for text streams 为文本流构建和可视化主题森林

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106455

Takayasu Fushimi, T. Satoh

{"title":"Constructing and visualizing topic forests for text streams","authors":"Takayasu Fushimi, T. Satoh","doi":"10.1145/3106426.3106455","DOIUrl":"https://doi.org/10.1145/3106426.3106455","url":null,"abstract":"A great deal of such texts as news and blog articles, web pages, and scientific literature are posted on the web as time goes by, and are generally called time-series documents or text streams. For each document, some strongly or weakly relevant texts exist. Although such relevance is represented as citations among scientific literatures, trackback among blog articles, hyperlinks among Wikipedia articles or web pages and so on, the relevance among news articles is not always clearly specified. One easy way to build a similarity network is by calculating the similarity among news articles and making links among similar articles; however, adding information about the posted times of articles to a similarity network is difficult. To overcome this problem, we propose a framework that consists of two parts: 1) tree structures called Topic Forests and 2) their visualization. Topic Forests are constructed by semantically and temporally linking cohesive texts while preserving their posted order. We provide effective access for users to text streams by embedding Topic Forests over the polar coordinates with a technique called Polar Coordinate Embedding. From experimental evaluations using the actual text streams of news articles, we confirm that Topic Forests semantically and temporally maintain cohesiveness, and Polar Coordinate Embedding achieves effective accessibility.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"156 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84991192","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: 1

Automated classification of EEG signals for predicting students' cognitive state during learning 脑电信号自动分类预测学生学习过程中的认知状态

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106453

Xi Liu, P. Tan, Lei Liu, S. Simske

{"title":"Automated classification of EEG signals for predicting students' cognitive state during learning","authors":"Xi Liu, P. Tan, Lei Liu, S. Simske","doi":"10.1145/3106426.3106453","DOIUrl":"https://doi.org/10.1145/3106426.3106453","url":null,"abstract":"For distance learning applications, inferring the cognitive states of students, particularly, their concentration and comprehension levels during instruction, is important to assess their learning efficacy. In this paper, we investigated the feasibility of using EEG recordings generated from an off-the-shelf, wearable device to automatically classify the cognitive states of students as they were asked to perform a series of reading and question answering tasks. We showed that the EEG data can effectively predict whether a student is attentive or distracted as well as the student's reading speed, which is an important measure of reading fluency. However, the EEG signals alone are insufficient to predict how well the students can correctly answer questions related to the reading materials as there were other confounding factors, such as the students' background knowledge, that must be taken into consideration. We also showed that the accuracy in predicting the different cognitive states depends on the choice of classifier used (global, local, or multi-task learning). For example, the concentration level of a student can be accurately predicted using a local model whereas a global model that incorporates side information about the student's background knowledge is more effective at predicting whether the student will correctly answer questions about the materials they read.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84865331","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: 16

CrimeProfiler: crime information extraction and visualization from news media CrimeProfiler:从新闻媒体中提取和可视化犯罪信息

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106476

Tirthankar Dasgupta, Abir Naskar, Rupsa Saha, Lipika Dey

{"title":"CrimeProfiler: crime information extraction and visualization from news media","authors":"Tirthankar Dasgupta, Abir Naskar, Rupsa Saha, Lipika Dey","doi":"10.1145/3106426.3106476","DOIUrl":"https://doi.org/10.1145/3106426.3106476","url":null,"abstract":"News articles from different sources regularly report crime incidents that contain details of crime, information about accused entities, details of the investigation process and finally details of judgement. In this paper, we have proposed natural language processing techniques for extraction and curation of crime-related information from digitally published News articles. We have leveraged computational linguistics based methods to analyse crime related News documents to extract different crime related entities and events. This includes name of the criminal, name of the victim, nature of crime, geographic location, date and time, and action taken against the criminal. We have also proposed a semi-supervised learning technique to learn different categories of crime events from the News documents. This helps in continuous evolution of the crime dictionaries. Thus the proposed methods are not restricted to detecting known crimes only but contribute actively towards maintaining an updated crime dictionary. We have done experiments with a collection of 3000 crime-reporting News articles. The end-product of our experiments is a crime-register that contains details of crime committed across geographies and time. This register can be further utilized for analytical and reporting purposes.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73510322","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: 16

Identifying active, reactive, and inactive targets of socialbots in Twitter 识别活跃的，被动的和不活跃的目标在Twitter上的社交机器人

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106483

Mohd Fazil, M. Abulaish

{"title":"Identifying active, reactive, and inactive targets of socialbots in Twitter","authors":"Mohd Fazil, M. Abulaish","doi":"10.1145/3106426.3106483","DOIUrl":"https://doi.org/10.1145/3106426.3106483","url":null,"abstract":"Online social networks are facing serious threats due to presence of human-behaviour imitating malicious bots (aka socialbots) that are successful mainly due to existence of their duped followers. In this paper, we propose an approach to categorize Twitter users into three groups - active, reactive, and inactive targets, based on their interaction behaviour with socialbots. Active users are those who themselves follow socialbots without being followed by them, reactive users respond to the following socialbots by following them back, whereas inactive users do not show any interest against the following requests from anonymous socialbots. The proposed approach is modelled as both binary and ternary classification problem, wherein users' profile is generated using static and dynamic components representing their identical and behavioural aspects. Three different classification techniques viz Naive Bayes, Reduced Error Pruned Decision Tree, and Random Forest are used over a dataset of 749 users collected through live experiment, and a thorough analyses of the identified users categories is presented, wherein it is found that active and reactive users keep on frequently updating their tweets containing advertising related contents. Finally, feature ranking algorithms are used to rank identified features to analyse their discriminative power, and it is found that following rate and follower rate are the most dominating features.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74951068","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: 11

Current location-based next POI recommendation 基于当前位置的下一个POI推荐

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106528

Shokirkhon Oppokhonov, Seyoung Park, Isaac. K. E. Ampomah

引用次数: 12

Sentiment diversification for short review summarization 情绪多元化短评总结

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106525

Mohammed Al-Dhelaan, A. Al-Suhaim

引用次数: 4

A logic for reasoning about evidence and belief 对证据和信念进行推理的逻辑

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106519

T. Fan, C. Liau

引用次数: 2