Moshe Koppel, Jonathan Schler, S. Argamon, Eran Messeri
In this paper, we use a blog corpus to demonstrate that we can often identify the author of an anonymous text even where there are many thousands of candidate authors. Our approach combines standard information retrieval methods with a text categorization meta-learning scheme that determines when to even venture a guess.
{"title":"Authorship attribution with thousands of candidate authors","authors":"Moshe Koppel, Jonathan Schler, S. Argamon, Eran Messeri","doi":"10.1145/1148170.1148304","DOIUrl":"https://doi.org/10.1145/1148170.1148304","url":null,"abstract":"In this paper, we use a blog corpus to demonstrate that we can often identify the author of an anonymous text even where there are many thousands of candidate authors. Our approach combines standard information retrieval methods with a text categorization meta-learning scheme that determines when to even venture a guess.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128066971","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}
Exhaustive evaluation of ranked queries can be expensive, particularly when only a small subset of the overall ranking is required, or when queries contain common terms. This concern gives rise to techniques for dynamic query pruning, that is, methods for eliminating redundant parts of the usual exhaustive evaluation, yet still generating a demonstrably "good enough" set of answers to the query. In this work we propose new pruning methods that make use of impact-sorted indexes. Compared to exhaustive evaluation, the new methods reduce the amount of computation performed, reduce the amount of memory required for accumulators, reduce the amount of data transferred from disk, and at the same time allow performance guarantees in terms of precision and mean average precision. These strong claims are backed by experiments using the TREC Terabyte collection and queries.
{"title":"Pruned query evaluation using pre-computed impacts","authors":"V. Anh, Alistair Moffat","doi":"10.1145/1148170.1148235","DOIUrl":"https://doi.org/10.1145/1148170.1148235","url":null,"abstract":"Exhaustive evaluation of ranked queries can be expensive, particularly when only a small subset of the overall ranking is required, or when queries contain common terms. This concern gives rise to techniques for dynamic query pruning, that is, methods for eliminating redundant parts of the usual exhaustive evaluation, yet still generating a demonstrably \"good enough\" set of answers to the query. In this work we propose new pruning methods that make use of impact-sorted indexes. Compared to exhaustive evaluation, the new methods reduce the amount of computation performed, reduce the amount of memory required for accumulators, reduce the amount of data transferred from disk, and at the same time allow performance guarantees in terms of precision and mean average precision. These strong claims are backed by experiments using the TREC Terabyte collection and queries.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"9 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130846893","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}
We present rpref ; our generalization of the bpref evaluation metric for assessing the quality of search engine results, given graded rather than binary user relevance judgments.
{"title":"Rpref: a generalization of Bpref towards graded relevance judgments","authors":"Jan De Beer, Marie-Francine Moens","doi":"10.1145/1148170.1148293","DOIUrl":"https://doi.org/10.1145/1148170.1148293","url":null,"abstract":"We present rpref ; our generalization of the bpref evaluation metric for assessing the quality of search engine results, given graded rather than binary user relevance judgments.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131365869","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}
We discuss information retrieval methods that aim at serving a diverse stream of user queries. We propose methods that emphasize the importance of taking into consideration of query difference in learning effective retrieval functions. We formulate the problem as a multi-task learning problem using a risk minimization framework. In particular, we show how to calibrate the empirical risk to incorporate query difference in terms of introducing nuisance parameters in the statistical models, and we also propose an alternating optimization method to simultaneously learn the retrieval function and the nuisance parameters. We illustrate the effectiveness of the proposed methods using modeling data extracted from a commercial search engine.
{"title":"Incorporating query difference for learning retrieval functions in information retrieval","authors":"H. Zha, Zhaohui Zheng, Haoying Fu, Gordon Sun","doi":"10.1145/1148170.1148335","DOIUrl":"https://doi.org/10.1145/1148170.1148335","url":null,"abstract":"We discuss information retrieval methods that aim at serving a diverse stream of user queries. We propose methods that emphasize the importance of taking into consideration of query difference in learning effective retrieval functions. We formulate the problem as a multi-task learning problem using a risk minimization framework. In particular, we show how to calibrate the empirical risk to incorporate query difference in terms of introducing nuisance parameters in the statistical models, and we also propose an alternating optimization method to simultaneously learn the retrieval function and the nuisance parameters. We illustrate the effectiveness of the proposed methods using modeling data extracted from a commercial search engine.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133841862","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}
Modern retrieval test collections are built through a process called pooling in which only a sample of the entire document set is judged for each topic. The idea behind pooling is to find enough relevant documents such that when unjudged documents are assumed to be nonrelevant the resulting judgment set is sufficiently complete and unbiased. As document sets grow larger, a constant-size pool represents an increasingly small percentage of the document set, and at some point the assumption of approximately complete judgments must become invalid.This paper demonstrates that the AQUAINT 2005 test collection exhibits bias caused by pools that were too shallow for the document set size despite having many diverse runs contribute to the pools. The existing judgment set favors relevant documents that contain topic title words even though relevant documents containing few topic title words are known to exist in the document set. The paper concludes with suggested modifications to traditional pooling and evaluation methodology that may allow very large reusable test collections to be built.
{"title":"Bias and the limits of pooling","authors":"C. Buckley, D. Dimmick, I. Soboroff, E. Voorhees","doi":"10.1145/1148170.1148284","DOIUrl":"https://doi.org/10.1145/1148170.1148284","url":null,"abstract":"Modern retrieval test collections are built through a process called pooling in which only a sample of the entire document set is judged for each topic. The idea behind pooling is to find enough relevant documents such that when unjudged documents are assumed to be nonrelevant the resulting judgment set is sufficiently complete and unbiased. As document sets grow larger, a constant-size pool represents an increasingly small percentage of the document set, and at some point the assumption of approximately complete judgments must become invalid.This paper demonstrates that the AQUAINT 2005 test collection exhibits bias caused by pools that were too shallow for the document set size despite having many diverse runs contribute to the pools. The existing judgment set favors relevant documents that contain topic title words even though relevant documents containing few topic title words are known to exist in the document set. The paper concludes with suggested modifications to traditional pooling and evaluation methodology that may allow very large reusable test collections to be built.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115313124","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":"Give me just one highly relevant document: P-measure","authors":"T. Sakai","doi":"10.1145/1148170.1148322","DOIUrl":"https://doi.org/10.1145/1148170.1148322","url":null,"abstract":"We introduce an evaluation metric called P-measure for the task of retrieving <ione highly relevant document. It models user behaviour in practical tasks such as known-item search, and is more stable and sensitive than Reciprocal Rank which cannot handle graded relevance.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121348357","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}
H. Blok, V. Mihajlović, G. Ramírez, T. Westerveld, D. Hiemstra, A. D. Vries
Not many XML information retrieval (IR) systems exist that allow easy addition of and switching between different IR models. Especially for the scientific environment where building a system takes a lot of time and keeps researchers away from the real work, i.e., investigating what is the most effective IR model, a platform that would provide this functionality would be ideal. For this reason we developed such an XML IR system. It is centered around a logical algebra, named score region algebra (SRA), that enables transparent specification of IR models for XML databases (see [1] for more details). The transparency is achieved by a possibility to instantiate various retrieval models, using abstract score functions within algebra operators, while logical query plan and operator definitions remain unchanged. Our algebra operators model three important aspects of XML IR: element relevance score computation, element score propagation, and element score combination. To implement a new IR model, one only needs to provide definitions for these abstract function classes. To illustrate the usefulness of our algebra our demo system supports several, well known IR scoring models (e.g., Language Models, Okapi, and tf.idf), combined with different score propagation and combination functions. The user can select which model to use at run time. Following good practice in database systems design, our prototype system has a typical three-layered architecture. (1) The conceptual layer takes a NEXI [3] query expression as input, e.g.,
{"title":"The TIJAH XML information retrieval system","authors":"H. Blok, V. Mihajlović, G. Ramírez, T. Westerveld, D. Hiemstra, A. D. Vries","doi":"10.1145/1148170.1148338","DOIUrl":"https://doi.org/10.1145/1148170.1148338","url":null,"abstract":"Not many XML information retrieval (IR) systems exist that allow easy addition of and switching between different IR models. Especially for the scientific environment where building a system takes a lot of time and keeps researchers away from the real work, i.e., investigating what is the most effective IR model, a platform that would provide this functionality would be ideal. For this reason we developed such an XML IR system. It is centered around a logical algebra, named score region algebra (SRA), that enables transparent specification of IR models for XML databases (see [1] for more details). The transparency is achieved by a possibility to instantiate various retrieval models, using abstract score functions within algebra operators, while logical query plan and operator definitions remain unchanged. Our algebra operators model three important aspects of XML IR: element relevance score computation, element score propagation, and element score combination. To implement a new IR model, one only needs to provide definitions for these abstract function classes. To illustrate the usefulness of our algebra our demo system supports several, well known IR scoring models (e.g., Language Models, Okapi, and tf.idf), combined with different score propagation and combination functions. The user can select which model to use at run time. Following good practice in database systems design, our prototype system has a typical three-layered architecture. (1) The conceptual layer takes a NEXI [3] query expression as input, e.g.,","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128788096","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 paper introduces a general framework for the use of translation probabilities in cross-language information retrieval based on the notion that information retrieval fundamentally requires matching what the searcher means with what the author of a document meant. That perspective yields a computational formulation that provides a natural way of combining what have been known as query and document translation. Two well-recognized techniques are shown to be a special case of this model under restrictive assumptions. Cross-language search results are reported that are statistically indistinguishable from strong monolingual baselines for both French and Chinese documents.
{"title":"Combining bidirectional translation and synonymy for cross-language information retrieval","authors":"Jianqiang Wang, Douglas W. Oard","doi":"10.1145/1148170.1148208","DOIUrl":"https://doi.org/10.1145/1148170.1148208","url":null,"abstract":"This paper introduces a general framework for the use of translation probabilities in cross-language information retrieval based on the notion that information retrieval fundamentally requires matching what the searcher means with what the author of a document meant. That perspective yields a computational formulation that provides a natural way of combining what have been known as query and document translation. Two well-recognized techniques are shown to be a special case of this model under restrictive assumptions. Cross-language search results are reported that are statistically indistinguishable from strong monolingual baselines for both French and Chinese documents.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"3476 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127505976","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 paper, we present a novel multi-webpage summarization algorithm. It adds the graph based ranking algorithm into the framework of Maximum Marginal Relevance (MMR) method, to not only capture the main topic of the web pages but also eliminate the redundancy existing in the sentences of the summary result. The experiment result indicates that the new approach has the better performance than the previous methods.
{"title":"A new web page summarization method","authors":"Q. Diao, Jiulong Shan","doi":"10.1145/1148170.1148294","DOIUrl":"https://doi.org/10.1145/1148170.1148294","url":null,"abstract":"In this paper, we present a novel multi-webpage summarization algorithm. It adds the graph based ranking algorithm into the framework of Maximum Marginal Relevance (MMR) method, to not only capture the main topic of the web pages but also eliminate the redundancy existing in the sentences of the summary result. The experiment result indicates that the new approach has the better performance than the previous methods.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117063662","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}
Methods for detecting sentences in an input document set, which are both relevant and novel with respect to an information need, would be of direct benefit to many systems, such as extractive text summarizers. However, satisfactory levels of agreement between judges performing this task manually have yet to demonstrated, leaving researchers to conclude that the task is too subjective. In previous experiments, judges were asked to first identify sentences that are relevant to a general topic, and then to eliminate sentences from the list that do not contain new information. Currently, a new task is proposed, in which annotators perform the same procedure, but within the context of a specific, factual information need. In the experiment, satisfactory levels of agreement between independent annotators were achieved on the first step of identifying sentences containing relevant information relevant. However, the results indicate that judges do not agree on which sentences contain novel information.
{"title":"Fact-focused novelty detection: a feasibility study","authors":"Jahna Otterbacher, Dragomir R. Radev","doi":"10.1145/1148170.1148318","DOIUrl":"https://doi.org/10.1145/1148170.1148318","url":null,"abstract":"Methods for detecting sentences in an input document set, which are both relevant and novel with respect to an information need, would be of direct benefit to many systems, such as extractive text summarizers. However, satisfactory levels of agreement between judges performing this task manually have yet to demonstrated, leaving researchers to conclude that the task is too subjective. In previous experiments, judges were asked to first identify sentences that are relevant to a general topic, and then to eliminate sentences from the list that do not contain new information. Currently, a new task is proposed, in which annotators perform the same procedure, but within the context of a specific, factual information need. In the experiment, satisfactory levels of agreement between independent annotators were achieved on the first step of identifying sentences containing relevant information relevant. However, the results indicate that judges do not agree on which sentences contain novel information.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121568633","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：