We show that a set of independently developed spam filters may be combined in simple ways to provide substantially better filtering than any of the individual filters. The results of fifty-three spam filters evaluated at the TREC 2005 Spam Track were combined post-hoc so as to simulate the parallel on-line operation of the filters. The combined results were evaluated using the TREC methodology, yielding more than a factor of two improvement over the best filter. The simplest method -- averaging the binary classifications returned by the individual filters -- yields a remarkably good result. A new method -- averaging log-odds estimates based on the scores returned by the individual filters -- yields a somewhat better result, and provides input to SVM- and logistic-regression-based stacking methods. The stacking methods appear to provide further improvement, but only for very large corpora. Of the stacking methods, logistic regression yields the better result. Finally, we show that it is possible to select a priori small subsets of the filters that, when combined, still outperform the best individual filter by a substantial margin.
{"title":"On-line spam filter fusion","authors":"T. Lynam, G. Cormack, D. Cheriton","doi":"10.1145/1148170.1148195","DOIUrl":"https://doi.org/10.1145/1148170.1148195","url":null,"abstract":"We show that a set of independently developed spam filters may be combined in simple ways to provide substantially better filtering than any of the individual filters. The results of fifty-three spam filters evaluated at the TREC 2005 Spam Track were combined post-hoc so as to simulate the parallel on-line operation of the filters. The combined results were evaluated using the TREC methodology, yielding more than a factor of two improvement over the best filter. The simplest method -- averaging the binary classifications returned by the individual filters -- yields a remarkably good result. A new method -- averaging log-odds estimates based on the scores returned by the individual filters -- yields a somewhat better result, and provides input to SVM- and logistic-regression-based stacking methods. The stacking methods appear to provide further improvement, but only for very large corpora. Of the stacking methods, logistic regression yields the better result. Finally, we show that it is possible to select a priori small subsets of the filters that, when combined, still outperform the best individual filter by a substantial margin.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"90 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":"134003505","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}
Xavier Sevillano, Germán Cobo, Francesc Alías, J. Socoró
The performance of document clustering systems depends on employing optimal text representations, which are not only difficult to determine beforehand, but also may vary from one clustering problem to another. As a first step towards building robust document clusterers, a strategy based on feature diversity and cluster ensembles is presented in this work. Experiments conducted on a binary clustering problem show that our method is robust to near-optimal model order selection and able to detect constructive interactions between different document representations in the test bed.
{"title":"Feature diversity in cluster ensembles for robust document clustering","authors":"Xavier Sevillano, Germán Cobo, Francesc Alías, J. Socoró","doi":"10.1145/1148170.1148323","DOIUrl":"https://doi.org/10.1145/1148170.1148323","url":null,"abstract":"The performance of document clustering systems depends on employing optimal text representations, which are not only difficult to determine beforehand, but also may vary from one clustering problem to another. As a first step towards building robust document clusterers, a strategy based on feature diversity and cluster ensembles is presented in this work. Experiments conducted on a binary clustering problem show that our method is robust to near-optimal model order selection and able to detect constructive interactions between different document representations in the test bed.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"116 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":"134100904","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 consider the following full-text search autocompletion feature. Imagine a user of a search engine typing a query. Then with every letter being typed, we would like an instant display of completions of the last query word which would lead to good hits. At the same time, the best hits for any of these completions should be displayed. Known indexing data structures that apply to this problem either incur large processing times for a substantial class of queries, or they use a lot of space. We present a new indexing data structure that uses no more space than a state-of-the-art compressed inverted index, but with 10 times faster query processing times. Even on the large TREC Terabyte collection, which comprises over 25 million documents, we achieve, on a single machine and with the index on disk, average response times of one tenth of a second. We have built a full-fledged, interactive search engine that realizes the proposed autocompletion feature combined with support for proximity search, semi-structured (XML) text, subword and phrase completion, and semantic tags.
{"title":"Type less, find more: fast autocompletion search with a succinct index","authors":"H. Bast, Ingmar Weber","doi":"10.1145/1148170.1148234","DOIUrl":"https://doi.org/10.1145/1148170.1148234","url":null,"abstract":"We consider the following full-text search autocompletion feature. Imagine a user of a search engine typing a query. Then with every letter being typed, we would like an instant display of completions of the last query word which would lead to good hits. At the same time, the best hits for any of these completions should be displayed. Known indexing data structures that apply to this problem either incur large processing times for a substantial class of queries, or they use a lot of space. We present a new indexing data structure that uses no more space than a state-of-the-art compressed inverted index, but with 10 times faster query processing times. Even on the large TREC Terabyte collection, which comprises over 25 million documents, we achieve, on a single machine and with the index on disk, average response times of one tenth of a second. We have built a full-fledged, interactive search engine that realizes the proposed autocompletion feature combined with support for proximity search, semi-structured (XML) text, subword and phrase completion, and semantic tags.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"53 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":"134376513","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 interactive question answering (QA), users and systems take turns to ask questions and provide answers. In such an interactive setting, user questions largely depend on the answers provided by the system. One question is whether user follow-up questions can provide feedback for the system to automatically assess its performance (e.g., assess whether a correct answer is delivered). This self-awareness can make QA systems more intelligent for information seeking, for example, by adapting better strategies to cope with problematic situations. Therefore, this paper describes our initial investigation in addressing this problem. Our results indicate that interaction context can provide useful cues for automated performance assessment in interactive QA.
{"title":"Automated performance assessment in interactive QA","authors":"J. Chai, Tyler Baldwin, Chen Zhang","doi":"10.1145/1148170.1148290","DOIUrl":"https://doi.org/10.1145/1148170.1148290","url":null,"abstract":"In interactive question answering (QA), users and systems take turns to ask questions and provide answers. In such an interactive setting, user questions largely depend on the answers provided by the system. One question is whether user follow-up questions can provide feedback for the system to automatically assess its performance (e.g., assess whether a correct answer is delivered). This self-awareness can make QA systems more intelligent for information seeking, for example, by adapting better strategies to cope with problematic situations. Therefore, this paper describes our initial investigation in addressing this problem. Our results indicate that interaction context can provide useful cues for automated performance assessment in interactive QA.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"10 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":"131731750","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 propose an integration of term proximity scoring into Okapi BM25. The relative retrieval effectiveness of our retrieval method, compared to pure BM25, varies from collection to collection.We present an experimental evaluation of our method and show that the gains achieved over BM25 as the size of the underlying text collection increases. We also show that for stemmed queries the impact of term proximity scoring is larger than for unstemmed queries.
{"title":"Term proximity scoring for ad-hoc retrieval on very large text collections","authors":"Stefan Büttcher, C. Clarke, Brad Lushman","doi":"10.1145/1148170.1148285","DOIUrl":"https://doi.org/10.1145/1148170.1148285","url":null,"abstract":"We propose an integration of term proximity scoring into Okapi BM25. The relative retrieval effectiveness of our retrieval method, compared to pure BM25, varies from collection to collection.We present an experimental evaluation of our method and show that the gains achieved over BM25 as the size of the underlying text collection increases. We also show that for stemmed queries the impact of term proximity scoring is larger than for unstemmed queries.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"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":"129373033","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 consider the problem of large-scale retrieval evaluation, and we propose a statistical method for evaluating retrieval systems using incomplete judgments. Unlike existing techniques that (1) rely on effectively complete, and thus prohibitively expensive, relevance judgment sets, (2) produce biased estimates of standard performance measures, or (3) produce estimates of non-standard measures thought to be correlated with these standard measures, our proposed statistical technique produces unbiased estimates of the standard measures themselves.Our proposed technique is based on random sampling. While our estimates are unbiased by statistical design, their variance is dependent on the sampling distribution employed; as such, we derive a sampling distribution likely to yield low variance estimates. We test our proposed technique using benchmark TREC data, demonstrating that a sampling pool derived from a set of runs can be used to efficiently and effectively evaluate those runs. We further show that these sampling pools generalize well to unseen runs. Our experiments indicate that highly accurate estimates of standard performance measures can be obtained using a number of relevance judgments as small as 4% of the typical TREC-style judgment pool.
{"title":"A statistical method for system evaluation using incomplete judgments","authors":"J. Aslam, Virgil Pavlu, Emine Yilmaz","doi":"10.1145/1148170.1148263","DOIUrl":"https://doi.org/10.1145/1148170.1148263","url":null,"abstract":"We consider the problem of large-scale retrieval evaluation, and we propose a statistical method for evaluating retrieval systems using incomplete judgments. Unlike existing techniques that (1) rely on effectively complete, and thus prohibitively expensive, relevance judgment sets, (2) produce biased estimates of standard performance measures, or (3) produce estimates of non-standard measures thought to be correlated with these standard measures, our proposed statistical technique produces unbiased estimates of the standard measures themselves.Our proposed technique is based on random sampling. While our estimates are unbiased by statistical design, their variance is dependent on the sampling distribution employed; as such, we derive a sampling distribution likely to yield low variance estimates. We test our proposed technique using benchmark TREC data, demonstrating that a sampling pool derived from a set of runs can be used to efficiently and effectively evaluate those runs. We further show that these sampling pools generalize well to unseen runs. Our experiments indicate that highly accurate estimates of standard performance measures can be obtained using a number of relevance judgments as small as 4% of the typical TREC-style judgment pool.","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":"131167344","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}
Xiaohua Zhou, Xiaohua Hu, Xiaodan Zhang, Xia Lin, I. Song
Semantic smoothing, which incorporates synonym and sense information into the language models, is effective and potentially significant to improve retrieval performance. The implemented semantic smoothing models, such as the translation model which statistically maps document terms to query terms, and a number of works that have followed have shown good experimental results. However, these models are unable to incorporate contextual information. Thus, the resulting translation might be mixed and fairly general. To overcome this limitation, we propose a novel context-sensitive semantic smoothing method that decomposes a document or a query into a set of weighted context-sensitive topic signatures and then translate those topic signatures into query terms. In detail, we solve this problem through (1) choosing concept pairs as topic signatures and adopting an ontology-based approach to extract concept pairs; (2) estimating the translation model for each topic signature using the EM algorithm; and (3) expanding document and query models based on topic signature translations. The new smoothing method is evaluated on TREC 2004/05 Genomics Track collections and significant improvements are obtained. The MAP (mean average precision) achieves a 33.6% maximal gain over the simple language model, as well as a 7.8% gain over the language model with context-insensitive semantic smoothing.
{"title":"Context-sensitive semantic smoothing for the language modeling approach to genomic IR","authors":"Xiaohua Zhou, Xiaohua Hu, Xiaodan Zhang, Xia Lin, I. Song","doi":"10.1145/1148170.1148203","DOIUrl":"https://doi.org/10.1145/1148170.1148203","url":null,"abstract":"Semantic smoothing, which incorporates synonym and sense information into the language models, is effective and potentially significant to improve retrieval performance. The implemented semantic smoothing models, such as the translation model which statistically maps document terms to query terms, and a number of works that have followed have shown good experimental results. However, these models are unable to incorporate contextual information. Thus, the resulting translation might be mixed and fairly general. To overcome this limitation, we propose a novel context-sensitive semantic smoothing method that decomposes a document or a query into a set of weighted context-sensitive topic signatures and then translate those topic signatures into query terms. In detail, we solve this problem through (1) choosing concept pairs as topic signatures and adopting an ontology-based approach to extract concept pairs; (2) estimating the translation model for each topic signature using the EM algorithm; and (3) expanding document and query models based on topic signature translations. The new smoothing method is evaluated on TREC 2004/05 Genomics Track collections and significant improvements are obtained. The MAP (mean average precision) achieves a 33.6% maximal gain over the simple language model, as well as a 7.8% gain over the language model with context-insensitive semantic smoothing.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"113 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120826599","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}
Search algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied since the 60s and has recently produced good results in the language model framework. An approach to building topic models based on a formal generative model of documents, Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use LDA to improve ad-hoc retrieval. We propose an LDA-based document model within the language modeling framework, and evaluate it on several TREC collections. Gibbs sampling is employed to conduct approximate inference in LDA and the computational complexity is analyzed. We show that improvements over retrieval using cluster-based models can be obtained with reasonable efficiency.
{"title":"LDA-based document models for ad-hoc retrieval","authors":"Xing Wei, W. Bruce Croft","doi":"10.1145/1148170.1148204","DOIUrl":"https://doi.org/10.1145/1148170.1148204","url":null,"abstract":"Search algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied since the 60s and has recently produced good results in the language model framework. An approach to building topic models based on a formal generative model of documents, Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use LDA to improve ad-hoc retrieval. We propose an LDA-based document model within the language modeling framework, and evaluate it on several TREC collections. Gibbs sampling is employed to conduct approximate inference in LDA and the computational complexity is analyzed. We show that improvements over retrieval using cluster-based models can be obtained with reasonable efficiency.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"144 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120873599","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}
Yunyao Li, R. Krishnamurthy, Shivakumar Vaithyanathan, H. Jagadish
Many searches on the web have a transactional intent. We argue that pages satisfying transactional needs can be distinguished from the more common pages that have some information and links, but cannot be used to execute a transaction. Based on this hypothesis, we provide a recipe for constructing a transaction annotator. By constructing an annotator with one corpus and then demonstrating its classification performance on another,we establish its robustness. Finally, we show experimentally that a search procedure that exploits such pre-annotation greatly outperforms traditional search for retrieving transactional pages.
{"title":"Getting work done on the web: supporting transactional queries","authors":"Yunyao Li, R. Krishnamurthy, Shivakumar Vaithyanathan, H. Jagadish","doi":"10.1145/1148170.1148266","DOIUrl":"https://doi.org/10.1145/1148170.1148266","url":null,"abstract":"Many searches on the web have a transactional intent. We argue that pages satisfying transactional needs can be distinguished from the more common pages that have some information and links, but cannot be used to execute a transaction. Based on this hypothesis, we provide a recipe for constructing a transaction annotator. By constructing an annotator with one corpus and then demonstrating its classification performance on another,we establish its robustness. Finally, we show experimentally that a search procedure that exploits such pre-annotation greatly outperforms traditional search for retrieving transactional pages.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"10 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":"123959958","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}
Márcio L. A. Vidal, A. D. Silva, E. Moura, J. Cavalcanti
Many Web IR and Digital Library applications require a crawling process to collect pages with the ultimate goal of taking advantage of useful information available on Web sites. For some of these applications the criteria to determine when a page is to be present in a collection are related to the page content. However, there are situations in which the inner structure of the pages provides a better criteria to guide the crawling process than their content. In this paper, we present a structure-driven approach for generating Web crawlers that requires a minimum effort from users. The idea is to take as input a sample page and an entry point to a Web site and generate a structure-driven crawler based on navigation patterns, sequences of patterns for the links a crawler has to follow to reach the pages structurally similar to the sample page. In the experiments we have carried out, structure-driven crawlers generated by our new approach were able to collect all pages that match the samples given, including those pages added after their generation.
{"title":"Structure-driven crawler generation by example","authors":"Márcio L. A. Vidal, A. D. Silva, E. Moura, J. Cavalcanti","doi":"10.1145/1148170.1148223","DOIUrl":"https://doi.org/10.1145/1148170.1148223","url":null,"abstract":"Many Web IR and Digital Library applications require a crawling process to collect pages with the ultimate goal of taking advantage of useful information available on Web sites. For some of these applications the criteria to determine when a page is to be present in a collection are related to the page content. However, there are situations in which the inner structure of the pages provides a better criteria to guide the crawling process than their content. In this paper, we present a structure-driven approach for generating Web crawlers that requires a minimum effort from users. The idea is to take as input a sample page and an entry point to a Web site and generate a structure-driven crawler based on navigation patterns, sequences of patterns for the links a crawler has to follow to reach the pages structurally similar to the sample page. In the experiments we have carried out, structure-driven crawlers generated by our new approach were able to collect all pages that match the samples given, including those pages added after their generation.","PeriodicalId":433366,"journal":{"name":"Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval","volume":"13 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":"125926534","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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