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

Fusing Search Results from Possible Alternative Queries 从可能的替代查询融合搜索结果

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0105

Ashraf Bah Rabiou, Ben Carterette

引用次数: 0

From Opinion Lexicons to Sentiment Classification of Tweets and Vice Versa: A Transfer Learning Approach 从观点词汇到推文的情感分类，反之亦然:一种迁移学习方法

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.29

Felipe Bravo-Marquez, E. Frank, B. Pfahringer

引用次数: 12

Context-Aware Entity Disambiguation in Text Using Markov Chains 基于马尔可夫链的文本上下文感知实体消歧

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0018

Lei Zhang, Achim Rettinger, Patrick Philipp

引用次数: 4

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

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

We Didn't Miss You: Interpolating Missing Opinions 我们没有错过你:插入缺失的意见

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0094

Iuliia Chepurna, M. Makrehchi

引用次数: 0

Context Free Frequently Asked Questions Detection Using Machine Learning Techniques 使用机器学习技术的无上下文常见问题检测

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0095

Fatemeh Razzaghi, Hamed Minaee, A. Ghorbani

{"title":"Context Free Frequently Asked Questions Detection Using Machine Learning Techniques","authors":"Fatemeh Razzaghi, Hamed Minaee, A. Ghorbani","doi":"10.1109/WI.2016.0095","DOIUrl":"https://doi.org/10.1109/WI.2016.0095","url":null,"abstract":"FAQs are the lists of common questions and answers on particular topics. Today one can find them in almost all web sites on the internet and they can be a great tool to give information to the users. Questions in FAQs are usually identified by the site administrators on the basis of the questions that are asked by their users. While such questions can respond to required information about a service, topic, or particular subject, they can not easily be distinguished from non-FAQ questions. This paper describes machine learning based parsing and question classification for FAQs. We demonstrate that questions for FAQs can be distinguished from other types of questions. Identification of specific features is the key to obtaining an accurate FAQ classifier. We propose a simple yet effective feature set including bag of words, lexical, syntactical, and semantic features. To evaluate our proposed methods, we gathered a large data set of FAQs in three different contexts, which were labeled by humans from real data. We showed that the SVM and Naive Bayes reach the accuracy of 80.3%, which is an outstanding result for the early stage research on FAQ classification. Experimental results show that the proposed approach can be a practical tool for question answering systems. To evaluate the accuracy of our classifier we have conducted an evaluation process and built the questionnaire. Therefore, we compared our classifier ranked questions with user rates and almost 81% similarity of the question ratings gives some confidence.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"27 1","pages":"558-561"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89789465","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: 5

Dynamic Model for Social Coalition Formation Based on Expertise, Temporal Reputation and Time Commitment 基于专业知识、时间声誉和时间承诺的社会联盟形成动态模型

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0052

C. Souza, F. Enembreck

{"title":"Dynamic Model for Social Coalition Formation Based on Expertise, Temporal Reputation and Time Commitment","authors":"C. Souza, F. Enembreck","doi":"10.1109/WI.2016.0052","DOIUrl":"https://doi.org/10.1109/WI.2016.0052","url":null,"abstract":"Existing approaches to coalition formation are generally gross simplifications of real problems of resource allocation where experience, reputation, and time optimization should be considered, although they are not usually studied together. To overcome this issue, this study proposes a dynamic and distributed social coalition formation model, that reproduces real-world environments where interactions are ruled by an underlying network that adapts itself based on the best updated reputation of local neighbors, in order to bring together individuals better suited for efficient cooperation. In this environment, agents possessing different levels of expertise must be organized to provide the most advantageous partnerships for the purpose of solving tasks, and an execution order of task's subtasks is defined to favor the use and release of agents' resources. To achieve this objective, we based our proposal on a coalitional skill game (CSG) approach, which organizes the use of resources by time commitment, and calculates and exploits the temporal reputation of heterogeneous agents to improve the utility of coalitions. Our experiments with different initial social networks allowed us to evaluate the effectiveness of this proposal and provided elements to exploit the advantages of an optimized social structure in a connected world.","PeriodicalId":6513,"journal":{"name":"2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)","volume":"31 1","pages":"319-326"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89681968","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

Exploring the World Languages in Twitter 在Twitter上探索世界语言

2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI) Pub Date : 2016-10-01 DOI: 10.1109/WI.2016.0031

P. Saha, R. Menezes

引用次数: 5

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