K. Balog, Yi Fang, M. de Rijke, P. Serdyukov, Luo Si
People have looked for experts since before the advent of computers. With advances in information retrieval technology and the large-scale availability of digital traces of knowledge-related activities, computer systems that can fully automate the process of locating expertise have become a reality. The past decade has witnessed tremendous interest, and a wealth of results, in expertise retrieval as an emerging subdiscipline in information retrieval. This survey highlights advances in models and algorithms relevant to this field. We draw connections among methods proposed in the literature and summarize them in five groups of basic approaches. These serve as the building blocks for more advanced models that arise when we consider a range of content-based factors that may impact the strength of association between a topic and a person. We also discuss practical aspects of building an expert search system and present applications of the technology in other domains, such as blog distillation and entity retrieval. The limitations of current approaches are also pointed out. We end our survey with a set of conjectures on what the future may hold for expertise retrieval research.
{"title":"Expertise Retrieval","authors":"K. Balog, Yi Fang, M. de Rijke, P. Serdyukov, Luo Si","doi":"10.1561/1500000024","DOIUrl":"https://doi.org/10.1561/1500000024","url":null,"abstract":"People have looked for experts since before the advent of computers. With advances in information retrieval technology and the large-scale availability of digital traces of knowledge-related activities, computer systems that can fully automate the process of locating expertise have become a reality. The past decade has witnessed tremendous interest, and a wealth of results, in expertise retrieval as an emerging subdiscipline in information retrieval. This survey highlights advances in models and algorithms relevant to this field. We draw connections among methods proposed in the literature and summarize them in five groups of basic approaches. These serve as the building blocks for more advanced models that arise when we consider a range of content-based factors that may impact the strength of association between a topic and a person. We also discuss practical aspects of building an expert search system and present applications of the technology in other domains, such as blog distillation and entity retrieval. The limitations of current approaches are also pointed out. We end our survey with a set of conjectures on what the future may hold for expertise retrieval research.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"2 1","pages":"127-256"},"PeriodicalIF":10.4,"publicationDate":"2012-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84257898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rodrygo L. T. Santos, C. Macdonald, R. McCreadie, I. Ounis, I. Soboroff
Blogs have recently emerged as a new open, rapidly evolving and reactive publishing medium on the Web. Rather than managed by a central entity, the content on the blogosphere — the collection of all blogs on the Web — is produced by millions of independent bloggers, who can write about virtually anything. This open publishing paradigm has led to a growing mass of user-generated content on the Web, which can vary tremendously both in format and quality when looked at in isolation, but which can also reveal interesting patterns when observed in aggregation. One field particularly interested in studying how information is produced, consumed, and searched in the blogosphere is information retrieval. In this survey, we review the published literature on searching the blogosphere. In particular, we describe the phenomenon of blogging and the motivations for searching for information on blogs. We cover both the search tasks underlying blog searchers' information needs and the most successful approaches to these tasks. These include blog post and full blog search tasks, as well as blog-aided search tasks, such as trend and market analysis. Finally, we also describe the publicly available resources that support research on searching the blogosphere.
{"title":"Information Retrieval on the Blogosphere","authors":"Rodrygo L. T. Santos, C. Macdonald, R. McCreadie, I. Ounis, I. Soboroff","doi":"10.1561/1500000026","DOIUrl":"https://doi.org/10.1561/1500000026","url":null,"abstract":"Blogs have recently emerged as a new open, rapidly evolving and reactive publishing medium on the Web. Rather than managed by a central entity, the content on the blogosphere — the collection of all blogs on the Web — is produced by millions of independent bloggers, who can write about virtually anything. This open publishing paradigm has led to a growing mass of user-generated content on the Web, which can vary tremendously both in format and quality when looked at in isolation, but which can also reveal interesting patterns when observed in aggregation. One field particularly interested in studying how information is produced, consumed, and searched in the blogosphere is information retrieval. In this survey, we review the published literature on searching the blogosphere. In particular, we describe the phenomenon of blogging and the motivations for searching for information on blogs. We cover both the search tasks underlying blog searchers' information needs and the most successful approaches to these tasks. These include blog post and full blog search tasks, as well as blog-aided search tasks, such as trend and market analysis. Finally, we also describe the publicly available resources that support research on searching the blogosphere.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"38 1","pages":"1-125"},"PeriodicalIF":10.4,"publicationDate":"2012-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76898614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR.
{"title":"Spoken Content Retrieval: A Survey of Techniques and Technologies","authors":"M. Larson, G. Jones","doi":"10.1561/1500000020","DOIUrl":"https://doi.org/10.1561/1500000020","url":null,"abstract":"Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"49 1","pages":"235-422"},"PeriodicalIF":10.4,"publicationDate":"2012-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73010469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Federated search (federated information retrieval or distributed information retrieval) is a technique for searching multiple text collections simultaneously. Queries are submitted to a subset of collections that are most likely to return relevant answers. The results returned by selected collections are integrated and merged into a single list. Federated search is preferred over centralized search alternatives in many environments. For example, commercial search engines such as Google cannot easily index uncrawlable hidden web collections while federated search systems can search the contents of hidden web collections without crawling. In enterprise environments, where each organization maintains an independent search engine, federated search techniques can provide parallel search over multiple collections. There are three major challenges in federated search. For each query, a subset of collections that are most likely to return relevant documents are selected. This creates the collection selection problem. To be able to select suitable collections, federated search systems need to acquire some knowledge about the contents of each collection, creating the collection representation problem. The results returned from the selected collections are merged before the final presentation to the user. This final step is the result merging problem. The goal of this work, is to provide a comprehensive summary of the previous research on the federated search challenges described above.
{"title":"Federated Search","authors":"Milad Shokouhi, Luo Si","doi":"10.1561/1500000010","DOIUrl":"https://doi.org/10.1561/1500000010","url":null,"abstract":"Federated search (federated information retrieval or distributed information retrieval) is a technique for searching multiple text collections simultaneously. Queries are submitted to a subset of collections that are most likely to return relevant answers. The results returned by selected collections are integrated and merged into a single list. Federated search is preferred over centralized search alternatives in many environments. For example, commercial search engines such as Google cannot easily index uncrawlable hidden web collections while federated search systems can search the contents of hidden web collections without crawling. In enterprise environments, where each organization maintains an independent search engine, federated search techniques can provide parallel search over multiple collections. \u0000 \u0000There are three major challenges in federated search. For each query, a subset of collections that are most likely to return relevant documents are selected. This creates the collection selection problem. To be able to select suitable collections, federated search systems need to acquire some knowledge about the contents of each collection, creating the collection representation problem. The results returned from the selected collections are merged before the final presentation to the user. This final step is the result merging problem. \u0000 \u0000The goal of this work, is to provide a comprehensive summary of the previous research on the federated search challenges described above.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"30 1","pages":"1-102"},"PeriodicalIF":10.4,"publicationDate":"2011-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77818433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Web search engines have become indispensable tools for finding content. As the popularity of the Web has increased, the efforts to exploit the Web for commercial, social, or political advantage have grown, making it harder for search engines to discriminate between truthful signals of content quality and deceptive attempts to game search engines' rankings. This problem is further complicated by the open nature of the Web, which allows anyone to write and publish anything, and by the fact that search engines must analyze ever-growing numbers of Web pages. Moreover, increasing expectations of users, who over time rely on Web search for information needs related to more aspects of their lives, further deepen the need for search engines to develop effective counter-measures against deception. In this monograph, we consider the effects of the adversarial relationship between search systems and those who wish to manipulate them, a field known as "Adversarial Information Retrieval". We show that search engine spammers create false content and misleading links to lure unsuspecting visitors to pages filled with advertisements or malware. We also examine work over the past decade or so that aims to discover such spamming activities to get spam pages removed or their effect on the quality of the results reduced. Research in Adversarial Information Retrieval has been evolving over time, and currently continues both in traditional areas (e.g., link spam) and newer areas, such as click fraud and spam in social media, demonstrating that this conflict is far from over.
{"title":"Adversarial Web Search","authors":"C. Castillo, Brian D. Davison","doi":"10.1561/1500000021","DOIUrl":"https://doi.org/10.1561/1500000021","url":null,"abstract":"Web search engines have become indispensable tools for finding content. As the popularity of the Web has increased, the efforts to exploit the Web for commercial, social, or political advantage have grown, making it harder for search engines to discriminate between truthful signals of content quality and deceptive attempts to game search engines' rankings. This problem is further complicated by the open nature of the Web, which allows anyone to write and publish anything, and by the fact that search engines must analyze ever-growing numbers of Web pages. Moreover, increasing expectations of users, who over time rely on Web search for information needs related to more aspects of their lives, further deepen the need for search engines to develop effective counter-measures against deception. \u0000 \u0000In this monograph, we consider the effects of the adversarial relationship between search systems and those who wish to manipulate them, a field known as \"Adversarial Information Retrieval\". We show that search engine spammers create false content and misleading links to lure unsuspecting visitors to pages filled with advertisements or malware. We also examine work over the past decade or so that aims to discover such spamming activities to get spam pages removed or their effect on the quality of the results reduced. \u0000 \u0000Research in Adversarial Information Retrieval has been evolving over time, and currently continues both in traditional areas (e.g., link spam) and newer areas, such as click fraud and spam in social media, demonstrating that this conflict is far from over.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"1 1","pages":"377-486"},"PeriodicalIF":10.4,"publicationDate":"2011-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80485931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It has now been 50 years since the publication of Luhn’s seminal paper on automatic summarization. During these years the practical need for automatic summarization has become increasingly urgent and numerous papers have been published on the topic. As a result, it has become harder to find a single reference that gives an overview of past efforts or a complete view of summarization tasks and necessary system components. This article attempts to fill this void by providing a comprehensive overview of research in summarization, including the more traditional efforts in sentence extraction as well as the most novel recent approaches for determining important content, for domain and genre specific summarization and for evaluation of summarization. We also discuss the challenges that remain open, in particular the need for language generation and deeper semantic understanding of language that would be necessary for future advances in the field. We would like to thank the anonymous reviewers, our students and Noemie Elhadad, Hongyan Jing, Julia Hirschberg, Annie Louis, Smaranda Muresan and Dragomir Radev for their helpful feedback. This paper was supported in part by the U.S. National Science Foundation (NSF) under IIS-05-34871 and CAREER 09-53445. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. Full text available at: http://dx.doi.org/10.1561/1500000015
{"title":"Automatic Summarization","authors":"A. Nenkova, S. Maskey, Yang Liu","doi":"10.1561/1500000015","DOIUrl":"https://doi.org/10.1561/1500000015","url":null,"abstract":"It has now been 50 years since the publication of Luhn’s seminal paper on automatic summarization. During these years the practical need for automatic summarization has become increasingly urgent and numerous papers have been published on the topic. As a result, it has become harder to find a single reference that gives an overview of past efforts or a complete view of summarization tasks and necessary system components. This article attempts to fill this void by providing a comprehensive overview of research in summarization, including the more traditional efforts in sentence extraction as well as the most novel recent approaches for determining important content, for domain and genre specific summarization and for evaluation of summarization. We also discuss the challenges that remain open, in particular the need for language generation and deeper semantic understanding of language that would be necessary for future advances in the field. We would like to thank the anonymous reviewers, our students and Noemie Elhadad, Hongyan Jing, Julia Hirschberg, Annie Louis, Smaranda Muresan and Dragomir Radev for their helpful feedback. This paper was supported in part by the U.S. National Science Foundation (NSF) under IIS-05-34871 and CAREER 09-53445. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. Full text available at: http://dx.doi.org/10.1561/1500000015","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"32 1","pages":"103-233"},"PeriodicalIF":10.4,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78665747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Use of test collections and evaluation measures to assess the effectiveness of information retrieval systems has its origins in work dating back to the early 1950s. Across the nearly 60 years since that work started, use of test collections is a de facto standard of evaluation. This monograph surveys the research conducted and explains the methods and measures devised for evaluation of retrieval systems, including a detailed look at the use of statistical significance testing in retrieval experimentation. This monograph reviews more recent examinations of the validity of the test collection approach and evaluation measures as well as outlining trends in current research exploiting query logs and live labs. At its core, the modern-day test collection is little different from the structures that the pioneering researchers in the 1950s and 1960s conceived of. This tutorial and review shows that despite its age, this long-standing evaluation method is still a highly valued tool for retrieval research.
{"title":"Test Collection Based Evaluation of Information Retrieval Systems","authors":"M. Sanderson","doi":"10.1561/1500000009","DOIUrl":"https://doi.org/10.1561/1500000009","url":null,"abstract":"Use of test collections and evaluation measures to assess the effectiveness of information retrieval systems has its origins in work dating back to the early 1950s. Across the nearly 60 years since that work started, use of test collections is a de facto standard of evaluation. This monograph surveys the research conducted and explains the methods and measures devised for evaluation of retrieval systems, including a detailed look at the use of statistical significance testing in retrieval experimentation. This monograph reviews more recent examinations of the validity of the test collection approach and evaluation measures as well as outlining trends in current research exploiting query logs and live labs. At its core, the modern-day test collection is little different from the structures that the pioneering researchers in the 1950s and 1960s conceived of. This tutorial and review shows that despite its age, this long-standing evaluation method is still a highly valued tool for retrieval research.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"62 3 1","pages":"247-375"},"PeriodicalIF":10.4,"publicationDate":"2010-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79770399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This is a survey of the science and practice of web crawling. While at first glance web crawling may appear to be merely an application of breadth-first-search, the truth is that there are many challenges ranging from systems concerns such as managing very large data structures to theoretical questions such as how often to revisit evolving content sources. This survey outlines the fundamental challenges and describes the state-of-the-art models and solutions. It also highlights avenues for future work.
{"title":"Web Crawling","authors":"Christopher Olston, Marc Najork","doi":"10.1561/1500000017","DOIUrl":"https://doi.org/10.1561/1500000017","url":null,"abstract":"This is a survey of the science and practice of web crawling. While at first glance web crawling may appear to be merely an application of breadth-first-search, the truth is that there are many challenges ranging from systems concerns such as managing very large data structures to theoretical questions such as how often to revisit evolving content sources. This survey outlines the fundamental challenges and describes the state-of-the-art models and solutions. It also highlights avenues for future work.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"20 1","pages":"175-246"},"PeriodicalIF":10.4,"publicationDate":"2010-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75351298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Web search engines have stored in their logs information about users since they started to operate. This information often serves many purposes. The primary focus of this survey is on introducing to the discipline of query mining by showing its foundations and by analyzing the basic algorithms and techniques that are used to extract useful knowledge from this (potentially) infinite source of information. We show how search applications may benefit from this kind of analysis by analyzing popular applications of query log mining and their influence on user experience. We conclude the paper by, briefly, presenting some of the most challenging current open problems in this field.
{"title":"Mining Query Logs: Turning Search Usage Data into Knowledge","authors":"F. Silvestri","doi":"10.1561/1500000013","DOIUrl":"https://doi.org/10.1561/1500000013","url":null,"abstract":"Web search engines have stored in their logs information about users since they started to operate. This information often serves many purposes. The primary focus of this survey is on introducing to the discipline of query mining by showing its foundations and by analyzing the basic algorithms and techniques that are used to extract useful knowledge from this (potentially) infinite source of information. We show how search applications may benefit from this kind of analysis by analyzing popular applications of query log mining and their influence on user experience. We conclude the paper by, briefly, presenting some of the most challenging current open problems in this field.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"1 1","pages":"1-174"},"PeriodicalIF":10.4,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91093428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we review 300 references on video retrieval, indicating when text-only solutions are unsatisfactory and showing the promising alternatives which are in majority concept-based. Therefore, central to our discussion is the notion of a semantic concept: an objective linguistic description of an observable entity. Specifically, we present our view on how its automated detection, selection under uncertainty, and interactive usage might solve the major scientific problem for video retrieval: the semantic gap. To bridge the gap, we lay down the anatomy of a concept-based video search engine. We present a component-wise decomposition of such an interdisciplinary multimedia system, covering influences from information retrieval, computer vision, machine learning, and human–computer interaction. For each of the components we review state-of-the-art solutions in the literature, each having different characteristics and merits. Because of these differences, we cannot understand the progress in video retrieval without serious evaluation efforts such as carried out in the NIST TRECVID benchmark. We discuss its data, tasks, results, and the many derived community initiatives in creating annotations and baselines for repeatable experiments. We conclude with our perspective on future challenges and opportunities.
{"title":"Concept-Based Video Retrieval","authors":"Cees G. M. Snoek, M. Worring","doi":"10.1561/1500000014","DOIUrl":"https://doi.org/10.1561/1500000014","url":null,"abstract":"In this paper, we review 300 references on video retrieval, indicating when text-only solutions are unsatisfactory and showing the promising alternatives which are in majority concept-based. Therefore, central to our discussion is the notion of a semantic concept: an objective linguistic description of an observable entity. Specifically, we present our view on how its automated detection, selection under uncertainty, and interactive usage might solve the major scientific problem for video retrieval: the semantic gap. To bridge the gap, we lay down the anatomy of a concept-based video search engine. We present a component-wise decomposition of such an interdisciplinary multimedia system, covering influences from information retrieval, computer vision, machine learning, and human–computer interaction. For each of the components we review state-of-the-art solutions in the literature, each having different characteristics and merits. Because of these differences, we cannot understand the progress in video retrieval without serious evaluation efforts such as carried out in the NIST TRECVID benchmark. We discuss its data, tasks, results, and the many derived community initiatives in creating annotations and baselines for repeatable experiments. We conclude with our perspective on future challenges and opportunities.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"10 1","pages":"215-322"},"PeriodicalIF":10.4,"publicationDate":"2009-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81589311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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