{"title":"Session details: Session 8B: Citations","authors":"M. Sanderson","doi":"10.1145/3255937","DOIUrl":"https://doi.org/10.1145/3255937","url":null,"abstract":"","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"11 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":"124078805","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}
{"title":"Session details: Session 3B: Social Media","authors":"C. Hauff","doi":"10.1145/3255922","DOIUrl":"https://doi.org/10.1145/3255922","url":null,"abstract":"","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"27 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":"126544339","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}
In this article, we propose a word-level classification model for automatically generating a Twitter-specific opinion lexicon from a corpus of unlabelled tweets. The tweets from the corpus are represented by two vectors: a bag-of-words vector and a semantic vector based on word-clusters. We propose a distributional representation for words by treating them as the centroids of the tweet vectors in which they appear. The lexicon generation is conducted by training a word-level classifier using these centroids to form the instance space and a seed lexicon to label the training instances. Experimental results show that the two types of tweet vectors complement each other in a statistically significant manner and that our generated lexicon produces significant improvements for tweet-level polarity classification.
{"title":"From Unlabelled Tweets to Twitter-specific Opinion Words","authors":"Felipe Bravo-Marquez, E. Frank, B. Pfahringer","doi":"10.1145/2766462.2767770","DOIUrl":"https://doi.org/10.1145/2766462.2767770","url":null,"abstract":"In this article, we propose a word-level classification model for automatically generating a Twitter-specific opinion lexicon from a corpus of unlabelled tweets. The tweets from the corpus are represented by two vectors: a bag-of-words vector and a semantic vector based on word-clusters. We propose a distributional representation for words by treating them as the centroids of the tweet vectors in which they appear. The lexicon generation is conducted by training a word-level classifier using these centroids to form the instance space and a seed lexicon to label the training instances. Experimental results show that the two types of tweet vectors complement each other in a statistically significant manner and that our generated lexicon produces significant improvements for tweet-level polarity classification.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"28 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":"132008712","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}
Eugene Agichtein, David Carmel, C. Clarke, Praveen K. Paritosh, D. Pelleg, Idan Szpektor
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
{"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}
This Tutorial+Workshop will discuss opportunities and challenges involved in using neuro-physiological tools/techniques (such as fMRI, fNIRS, EEG, eye-tracking, GSR, HR, and facial expressions) and theories in information retrieval. The hybrid format will engage researchers and students at different levels of expertise, from those who are active in this area to those who are interested and want to learn more. The workshop will combine presentations, discussions and tutorial elements and consist of four segments (tutorial, completed research, work-in-progress, closing panel).
{"title":"NeuroIR 2015: Neuro-Physiological Methods in IR Research","authors":"J. Gwizdka, J. Jose, Javed Mostafa, Max L. Wilson","doi":"10.1145/2766462.2767856","DOIUrl":"https://doi.org/10.1145/2766462.2767856","url":null,"abstract":"This Tutorial+Workshop will discuss opportunities and challenges involved in using neuro-physiological tools/techniques (such as fMRI, fNIRS, EEG, eye-tracking, GSR, HR, and facial expressions) and theories in information retrieval. The hybrid format will engage researchers and students at different levels of expertise, from those who are active in this area to those who are interested and want to learn more. The workshop will combine presentations, discussions and tutorial elements and consist of four segments (tutorial, completed research, work-in-progress, closing panel).","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"103 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":"134594409","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}
In this introductory tutorial we give an overview of click models for web search. We show how the framework of probabilistic graphical models help to explain user behavior, build new evaluation metrics and perform simulations. The tutorial is augmented with a live demo where participants have a chance to implement a click model and to test it on a publicly available dataset.
{"title":"An Introduction to Click Models for Web Search: SIGIR 2015 Tutorial","authors":"A. Chuklin, I. Markov, M. de Rijke","doi":"10.1145/2766462.2767881","DOIUrl":"https://doi.org/10.1145/2766462.2767881","url":null,"abstract":"In this introductory tutorial we give an overview of click models for web search. We show how the framework of probabilistic graphical models help to explain user behavior, build new evaluation metrics and perform simulations. The tutorial is augmented with a live demo where participants have a chance to implement a click model and to test it on a publicly available dataset.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"7 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":"133927528","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}
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.
{"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}
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.
{"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}
This tutorial aims to provide attendees with a detailed understanding of end-to-end evaluation pipeline based on human judgments (offline measurement). The tutorial will give an overview of the state of the art methods, techniques, and metrics necessary for each stage of evaluation process. We will mostly focus on evaluating an information retrieval (search) system, but the other tasks such as recommendation and classification will also be discussed. Practical examples will be drawn both from the literature and from real world usage scenarios in industry.
{"title":"IR Evaluation: Designing an End-to-End Offline Evaluation Pipeline","authors":"Jin Young Kim, Emine Yilmaz","doi":"10.1145/2766462.2767875","DOIUrl":"https://doi.org/10.1145/2766462.2767875","url":null,"abstract":"This tutorial aims to provide attendees with a detailed understanding of end-to-end evaluation pipeline based on human judgments (offline measurement). The tutorial will give an overview of the state of the art methods, techniques, and metrics necessary for each stage of evaluation process. We will mostly focus on evaluating an information retrieval (search) system, but the other tasks such as recommendation and classification will also be discussed. Practical examples will be drawn both from the literature and from real world usage scenarios in industry.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"71 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":"125197979","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}
Yingwei Pan, Ting Yao, Houqiang Li, C. Ngo, Tao Mei
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.
{"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}
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