2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)最新文献_第10页

Detecting the Magnitude of Events from News Articles 从新闻文章中检测事件的大小

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0034

Ameeta Agrawal, Raghavender Sahdev, Heydar Davoudi, Forouq Khonsari, Aijun An, Susan McGrath

{"title":"Detecting the Magnitude of Events from News Articles","authors":"Ameeta Agrawal, Raghavender Sahdev, Heydar Davoudi, Forouq Khonsari, Aijun An, Susan McGrath","doi":"10.1109/WI.2016.0034","DOIUrl":"https://doi.org/10.1109/WI.2016.0034","url":null,"abstract":"Forced migration is increasingly becoming a global issue of concern. In this paper, we present an effective model of targeted event detection, as an essential step towards the forced migration detection problem. To date, most of the the approaches deal with the event detection in a general setting with the main objective of detecting the presence or onset of an event. However, we focus on analyzing the magnitude of a given event from a collection of text documents such as news articles from multiple sources. We use violence as an illustration as it is one of the most critical factors of forced migration. The recent advancements in semantic similarity measures are adopted to obtain relevant violence scores for each word in the vocabulary of news articles in an unsupervised manner. The resulting scores are then used to compute the average daily violence scores over a period of three months. Evaluation of the proposed model against a manually annotated data set yields a Pearson's correlation of 0.8. We also include a case study exploring the relationship between violence and key events.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"47 1","pages":"177-184"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78737292","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

Classification via Hidden Markov Trees for a Vision-Based Approach to Conveying Webpages to Users with Assistive Needs 基于视觉的隐马尔可夫树分类方法向有辅助需求的用户传递网页

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0124

M. Cormier, R. Mann, R. Cohen, Karyn Moffatt

引用次数: 5

Compositional Recurrent Neural Networks for Chinese Short Text Classification 中文短文本分类的组合递归神经网络

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0029

Yujun Zhou, Bo Xu, Jiaming Xu, Lei Yang, Changliang Li, Bo Xu

{"title":"Compositional Recurrent Neural Networks for Chinese Short Text Classification","authors":"Yujun Zhou, Bo Xu, Jiaming Xu, Lei Yang, Changliang Li, Bo Xu","doi":"10.1109/WI.2016.0029","DOIUrl":"https://doi.org/10.1109/WI.2016.0029","url":null,"abstract":"Word segmentation is the first step in Chinese natural language processing, and the error caused by word segmentation can be transmitted to the whole system. In order to reduce the impact of word segmentation and improve the overall performance of Chinese short text classification system, we propose a hybrid model of character-level and word-level features based on recurrent neural network (RNN) with long short-term memory (LSTM). By integrating character-level feature into word-level feature, the missing semantic information by the error of word segmentation will be constructed, meanwhile the wrong semantic relevance will be reduced. The final feature representation is that it suppressed the error of word segmentation in the case of maintaining most of the semantic features of the sentence. The whole model is finally trained end-to-end with supervised Chinese short text classification task. Results demonstrate that the proposed model in this paper is able to represent Chinese short text effectively, and the performances of 32-class and 5-class categorization outperform some remarkable methods.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"7 1","pages":"137-144"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90336560","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: 52

Recommendation System Based on Prediction of User Preference Changes 基于用户偏好变化预测的推荐系统

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0036

Kenta Inuzuka, Tomonori Hayashi, T. Takagi

{"title":"Recommendation System Based on Prediction of User Preference Changes","authors":"Kenta Inuzuka, Tomonori Hayashi, T. Takagi","doi":"10.1109/WI.2016.0036","DOIUrl":"https://doi.org/10.1109/WI.2016.0036","url":null,"abstract":"Time always exists in our lives and time data can easily be collected in a variety of applications. For example, when you purchase items online or click on an ad, the time at which you chose the item or clicked the ad is recorded. The analysis of time information can therefore be applied in various areas. It is important to note that user preferences change over time. For example, a person who watched animated TV shows in childhood will most likely switch to watching the news in adulthood. It is effective to incorporate such changes into recommender systems. In this paper, we propose an approach that predicts user preferences with consideration of preference changes by learning the order of purchase history in a recommender system. Our approach is composed of three steps. First, we obtain user features based on matrix factorization and purchasing time. Next, we use a Kalman filter to predict user preference vectors from user features. Finally, we generate a recommendation list, at which time we propose two types of recommendation methods using the predicted vectors. We then show through experiments using a real-world dataset that our approach outperforms competitive methods such as the first order Markov model.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"5 1","pages":"192-199"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74322285","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

A Research on Sentence Similarity for Question Answering System Based on Multi-feature Fusion 基于多特征融合的问答系统句子相似度研究

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0085

Haipeng Ruan, Yuan Li, Qinling Wang, Yu Liu

引用次数: 9

Generic Framework to Predict Repeat Behavior of Customers Using Their Transaction History 使用交易历史预测客户重复行为的通用框架

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0072

Auon Haidar Kazmi, Gautam M. Shroff, P. Agarwal

{"title":"Generic Framework to Predict Repeat Behavior of Customers Using Their Transaction History","authors":"Auon Haidar Kazmi, Gautam M. Shroff, P. Agarwal","doi":"10.1109/WI.2016.0072","DOIUrl":"https://doi.org/10.1109/WI.2016.0072","url":null,"abstract":"There exists a class of problems in e-commerce and retail businesses where the shopping behavior of customers is analyzed in order to predict their repeat behavior for products or retail stores. This analysis plays a crucial role in advertisement budgeting, product placement and relevant customer targeting. Researchers have addressed this problem by using standard predictive models, which use ad hoc features. We propose a metamodel that abstracts the different dimensions of data present in transactional datasets. These dimensions can be customer, product, offer, target, marketplace and transactions. Our framework also has abstract functions for comprehensive feature set generation, and includes different machine learning algorithms to learn prediction model. Our framework works end-to-end from feature engineering to reporting repeat probabilities of customers for products (or marketplace, brand, website or storechain). Moreover, the predicted repeat behavior of customers for different products along with their transactional history is used by our offer optimization model i-Prescribe to suggest products to be offered to customers with the goal of maximizing the return on investment of given marketing budget. We prove that our abstract features work on two different data-challenge datasets, by sharing experimental results.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"263 1","pages":"449-452"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76416714","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: 9

An Analysis of Main Solutions for the Automatic Construction of Ontologies from Text 文本本体自动构建的主要解决方案分析

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0074

R. Girardi

引用次数: 0

Sensing Real-World Events Using Social Media Data and a Classification-Clustering Framework 使用社交媒体数据和分类聚类框架感知现实世界事件

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0039

Nasser Alsaedi, P. Burnap, O. Rana

引用次数: 6

Dynamic Allocation of Service Function Chains under Priority Dependency Constraint 优先依赖约束下业务功能链的动态分配

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0122

M. Masoud, Sanghoon Lee, S. Belkasim

引用次数: 5

Learning Processes Based on Data Sources with Certainty Levels in Linked Open Data 基于关联开放数据中具有确定性级别的数据源的学习过程

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0068

Jesse Xi Chen, M. Reformat, R. Yager

引用次数: 2