Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval最新文献_第2页

Session details: Session 8B: Citations 会话详细信息:会话8B:引文

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/3255937

M. Sanderson

引用次数: 0

Session details: Session 3B: Social Media 会议详情:第三部分:社交媒体

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/3255922

C. Hauff

引用次数: 0

From Unlabelled Tweets to Twitter-specific Opinion Words 从未标记的推文到推特特定的意见词

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767770

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

引用次数: 22

Web Question Answering: Beyond Factoids: SIGIR 2015 Workshop 网络问答:超越Factoids: SIGIR 2015研讨会

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767861

Eugene Agichtein, David Carmel, C. Clarke, Praveen K. Paritosh, D. Pelleg, Idan Szpektor

{"title":"Web Question Answering: Beyond Factoids: SIGIR 2015 Workshop","authors":"Eugene Agichtein, David Carmel, C. Clarke, Praveen K. Paritosh, D. Pelleg, Idan Szpektor","doi":"10.1145/2766462.2767861","DOIUrl":"https://doi.org/10.1145/2766462.2767861","url":null,"abstract":"Automatic question answering is a central topic in information retrieval. Web search engines have made great progress at answering factoid queries, such as “how many people live in Australia?”. These can provide a succinct answer, up to a few words in length, and sometimes offer additional information such as related facts or entities. However, for deeper questions which could benefit from a longer response (e.g., “history of Australia”), current search engine resort to returning a link to a detailed web document. Alternatively, such a question might be posted on a Community Question Answering (CQA) site (“Visiting Australia in May, what should I see?”), hoping to get a human authored and detailed response. In this workshop we aim to explore the boundaries of Web question answering to better understand the spectrum of approaches and possible responses that are more detailed than a short fact, yet are more useful than a full document result. Is it possible to automatically answer diverse questions ranging from advice on fixing a broken sink to requests for opinions on the best basketball player of all time. In addition, questions submitted on the Web can be either short and ambiguous (such as Web queries to a search engine), or long and detailed (such as CQA questions). This workshop is particularly timely for two additional reasons: (1) there still exist many disagreements regarding the goals and nature of Web question answering services, mostly relating to the questions of “question intent” (what kind of queries benefit from question answering compared to other methods); and (2) leading search engines are eager to provide","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130217262","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: 3

NeuroIR 2015: Neuro-Physiological Methods in IR Research NeuroIR 2015: IR研究中的神经生理学方法

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767856

J. Gwizdka, J. Jose, Javed Mostafa, Max L. Wilson

引用次数: 1

An Introduction to Click Models for Web Search: SIGIR 2015 Tutorial Web搜索的点击模型介绍:SIGIR 2015教程

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767881

A. Chuklin, I. Markov, M. de Rijke

引用次数: 56

CricketLinking: Linking Event Mentions from Cricket Match Reports to Ball Entities in Commentaries CricketLinking:将板球比赛报告中的事件提到链接到评论中的球实体

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767865

Manish Gupta

{"title":"CricketLinking: Linking Event Mentions from Cricket Match Reports to Ball Entities in Commentaries","authors":"Manish Gupta","doi":"10.1145/2766462.2767865","DOIUrl":"https://doi.org/10.1145/2766462.2767865","url":null,"abstract":"The 2011 Cricket World Cup final match was watched by around 135 million people. Such a huge viewership demands a great experience for users of online cricket portals. Many portals like espncricinfo.com host a variety of content related to recent matches including match reports and ball-by-ball commentaries. When reading a match report, reader experience can be significantly improved by augmenting (on demand) the event mentions in the report with detailed commentaries. We build an event linking system emph{CricketLinking} which first identifies event mentions from the reports and then links them to a set of balls. Finding linkable mentions is challenging because unlike entity linking problem settings, we do not have a concrete set of event entities to link to. Further, depending on the event type, event mentions could be linked to a single ball, or to a set of balls. Hence, identifying mention type as well as linking becomes challenging. We use a large number of domain specific features to learn classifiers for mention and mention type detection. Further, we leverage structured match, context similarity and sequential proximity to perform accurate linking. Finally, context based summarization is performed to provide a concise briefing of linked balls to each mention.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133934077","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: 4

A Random Walk Model for Optimization of Search Impact in Web Frontier Ranking 网页前沿排名中搜索影响优化的随机游走模型

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767737

G. Tran, Ata Turk, B. B. Cambazoglu, W. Nejdl

{"title":"A Random Walk Model for Optimization of Search Impact in Web Frontier Ranking","authors":"G. Tran, Ata Turk, B. B. Cambazoglu, W. Nejdl","doi":"10.1145/2766462.2767737","DOIUrl":"https://doi.org/10.1145/2766462.2767737","url":null,"abstract":"Large-scale web search engines need to crawl the Web continuously to discover and download newly created web content. The speed at which the new content is discovered and the quality of the discovered content can have a big impact on the coverage and quality of the results provided by the search engine. In this paper, we propose a search-centric solution to the problem of prioritizing the pages in the frontier of a crawler for download. Our approach essentially orders the web pages in the frontier through a random walk model that takes into account the pages' potential impact on user-perceived search quality. In addition, we propose a link graph enrichment technique that extends this solution. Finally, we explore a machine learning approach that combines different frontier prioritization approaches. We conduct experiments using two very large, real-life web datasets to observe various search quality metrics. Comparisons with several baseline techniques indicate that the proposed approaches have the potential to improve the user-perceived quality of web search results considerably.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131518655","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

IR Evaluation: Designing an End-to-End Offline Evaluation Pipeline IR评估:设计一个端到端的离线评估管道

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767875

Jin Young Kim, Emine Yilmaz

引用次数: 2

Semi-supervised Hashing with Semantic Confidence for Large Scale Visual Search 基于语义置信度的半监督哈希大规模视觉搜索

Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval Pub Date : 2015-08-09 DOI: 10.1145/2766462.2767725

Yingwei Pan, Ting Yao, Houqiang Li, C. Ngo, Tao Mei

{"title":"Semi-supervised Hashing with Semantic Confidence for Large Scale Visual Search","authors":"Yingwei Pan, Ting Yao, Houqiang Li, C. Ngo, Tao Mei","doi":"10.1145/2766462.2767725","DOIUrl":"https://doi.org/10.1145/2766462.2767725","url":null,"abstract":"Similarity search is one of the fundamental problems for large scale multimedia applications. Hashing techniques, as one popular strategy, have been intensively investigated owing to the speed and memory efficiency. Recent research has shown that leveraging supervised information can lead to high quality hashing. However, most existing supervised methods learn hashing function by treating each training example equally while ignoring the different semantic degree related to the label, i.e. semantic confidence, of different examples. In this paper, we propose a novel semi-supervised hashing framework by leveraging semantic confidence. Specifically, a confidence factor is first assigned to each example by neighbor voting and click count in the scenarios with label and click-through data, respectively. Then, the factor is incorporated into the pairwise and triplet relationship learning for hashing. Furthermore, the two learnt relationships are seamlessly encoded into semi-supervised hashing methods with pairwise and listwise supervision respectively, which are formulated as minimizing empirical error on the labeled data while maximizing the variance of hash bits or minimizing quantization loss over both the labeled and unlabeled data. In addition, the kernelized variant of semi-supervised hashing is also presented. We have conducted experiments on both CIFAR-10 (with label) and Clickture (with click data) image benchmarks (up to one million image examples), demonstrating that our approaches outperform the state-of-the-art hashing techniques.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131051704","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: 34