Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management最新文献
S. Sanner, Shengbo Guo, T. Graepel, S. Kharazmi, Sarvnaz Karimi
It has been previously observed that optimization of the 1-call@k relevance objective (i.e., a set-based objective that is 1 if at least one document is relevant, otherwise 0) empirically correlates with diverse retrieval. In this paper, we proceed one step further and show theoretically that greedily optimizing expected 1-call@k w.r.t. a latent subtopic model of binary relevance leads to a diverse retrieval algorithm sharing many features of existing diversification approaches. This new result is complementary to a variety of diverse retrieval algorithms derived from alternate rank-based relevance criteria such as average precision and reciprocal rank. As such, the derivation presented here for expected 1-call@k provides a novel theoretical perspective on the emergence of diversity via a latent subtopic model of relevance --- an idea underlying both ambiguous and faceted subtopic retrieval that have been used to motivate diverse retrieval.
{"title":"Diverse retrieval via greedy optimization of expected 1-call@k in a latent subtopic relevance model","authors":"S. Sanner, Shengbo Guo, T. Graepel, S. Kharazmi, Sarvnaz Karimi","doi":"10.1145/2063576.2063869","DOIUrl":"https://doi.org/10.1145/2063576.2063869","url":null,"abstract":"It has been previously observed that optimization of the 1-call@k relevance objective (i.e., a set-based objective that is 1 if at least one document is relevant, otherwise 0) empirically correlates with diverse retrieval. In this paper, we proceed one step further and show theoretically that greedily optimizing expected 1-call@k w.r.t. a latent subtopic model of binary relevance leads to a diverse retrieval algorithm sharing many features of existing diversification approaches. This new result is complementary to a variety of diverse retrieval algorithms derived from alternate rank-based relevance criteria such as average precision and reciprocal rank. As such, the derivation presented here for expected 1-call@k provides a novel theoretical perspective on the emergence of diversity via a latent subtopic model of relevance --- an idea underlying both ambiguous and faceted subtopic retrieval that have been used to motivate diverse retrieval.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"38 1","pages":"1977-1980"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77534922","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}
Can Wang, Longbing Cao, Mingchun Wang, Jinjiu Li, Wei Wei, Yuming Ou
The similarity between nominal objects is not straightforward, especially in unsupervised learning. This paper proposes coupled similarity metrics for nominal objects, which consider not only intra-coupled similarity within an attribute (i.e., value frequency distribution) but also inter-coupled similarity between attributes (i.e. feature dependency aggregation). Four metrics are designed to calculate the inter-coupled similarity between two categorical values by considering their relationships with other attributes. The theoretical analysis reveals their equivalent accuracy and superior efficiency based on intersection against others, in particular for large-scale data. Substantial experiments on extensive UCI data sets verify the theoretical conclusions. In addition, experiments of clustering based on the derived dissimilarity metrics show a significant performance improvement.
{"title":"Coupled nominal similarity in unsupervised learning","authors":"Can Wang, Longbing Cao, Mingchun Wang, Jinjiu Li, Wei Wei, Yuming Ou","doi":"10.1145/2063576.2063715","DOIUrl":"https://doi.org/10.1145/2063576.2063715","url":null,"abstract":"The similarity between nominal objects is not straightforward, especially in unsupervised learning. This paper proposes coupled similarity metrics for nominal objects, which consider not only intra-coupled similarity within an attribute (i.e., value frequency distribution) but also inter-coupled similarity between attributes (i.e. feature dependency aggregation). Four metrics are designed to calculate the inter-coupled similarity between two categorical values by considering their relationships with other attributes. The theoretical analysis reveals their equivalent accuracy and superior efficiency based on intersection against others, in particular for large-scale data. Substantial experiments on extensive UCI data sets verify the theoretical conclusions. In addition, experiments of clustering based on the derived dissimilarity metrics show a significant performance improvement.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"35 1","pages":"973-978"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79780462","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}
Raynor Vliegendhart, M. Larson, Christoph Kofler, J. Pouwelse
We investigate term clouds that represent the content available in a peer-to-peer (P2P) network. Such network term clouds are non-trivial to generate in distributed settings. Our term cloud generator was implemented and released in Tribler--a widely-used, server-free P2P system--to support users in understanding the sorts of content available. Our evaluation and analysis focuses on three aspects of the clouds: coverage, usefulness and accumulation speed. A live experiment demonstrates that individual peers accumulate substantial network-level information, indicating good coverage of the overall content of the system. The results of a user study carried out on a crowdsourcing platform confirm the usefulness of clouds, showing that they succeed in conveying to users information on the type of content available in the network. An analysis of five example peers reveals that accumulation speeds of terms at new peers can support the development of a semantically diverse term set quickly after a cold start. This work represents the first investigation of term clouds in a live, 100% server-free P2P setting.
{"title":"A peer's-eye view: network term clouds in a peer-to-peer system","authors":"Raynor Vliegendhart, M. Larson, Christoph Kofler, J. Pouwelse","doi":"10.1145/2063576.2063852","DOIUrl":"https://doi.org/10.1145/2063576.2063852","url":null,"abstract":"We investigate term clouds that represent the content available in a peer-to-peer (P2P) network. Such network term clouds are non-trivial to generate in distributed settings. Our term cloud generator was implemented and released in Tribler--a widely-used, server-free P2P system--to support users in understanding the sorts of content available. Our evaluation and analysis focuses on three aspects of the clouds: coverage, usefulness and accumulation speed. A live experiment demonstrates that individual peers accumulate substantial network-level information, indicating good coverage of the overall content of the system. The results of a user study carried out on a crowdsourcing platform confirm the usefulness of clouds, showing that they succeed in conveying to users information on the type of content available in the network. An analysis of five example peers reveals that accumulation speeds of terms at new peers can support the development of a semantically diverse term set quickly after a cold start. This work represents the first investigation of term clouds in a live, 100% server-free P2P setting.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"31 1","pages":"1909-1912"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80130611","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 a novel system TEXplorer that integrates keyword-based object ranking with the aggregation and exploration power of OLAP in a text database with rich structured attributes available, e.g., a product review database. TEXplorer can be implemented within a multi-dimensional text database, where each row is associated with structural dimensions (attributes) and text data (e.g., a document). The system utilizes the text cube data model, where a cell aggregates a set of documents with matching values in a subset of dimensions. Cells in a text cube capture different levels of summarization of the documents, and can represent objects at different conceptual levels. Users query the system by submitting a set of keywords. Instead of returning a ranked list of all the cells, we propose a keyword-based interactive exploration framework that could offer flexible OLAP navigational guides and help users identify the levels and objects they are interested in. A novel significance measure of dimensions is proposed based on the distribution of IR relevance of cells. During each interaction stage, dimensions are ranked according to their significance scores to guide drilling down; and cells in the same cuboids are ranked according to their relevance to guide exploration. We propose efficient algorithms and materialization strategies for ranking top-k dimensions and cells. Finally, extensive experiments on real datasets demonstrate the efficiency and effectiveness of our approach.
{"title":"TEXplorer: keyword-based object search and exploration in multidimensional text databases","authors":"Bo Zhao, C. Lin, Bolin Ding, Jiawei Han","doi":"10.1145/2063576.2063822","DOIUrl":"https://doi.org/10.1145/2063576.2063822","url":null,"abstract":"We propose a novel system TEXplorer that integrates keyword-based object ranking with the aggregation and exploration power of OLAP in a text database with rich structured attributes available, e.g., a product review database. TEXplorer can be implemented within a multi-dimensional text database, where each row is associated with structural dimensions (attributes) and text data (e.g., a document). The system utilizes the text cube data model, where a cell aggregates a set of documents with matching values in a subset of dimensions. Cells in a text cube capture different levels of summarization of the documents, and can represent objects at different conceptual levels.\u0000 Users query the system by submitting a set of keywords. Instead of returning a ranked list of all the cells, we propose a keyword-based interactive exploration framework that could offer flexible OLAP navigational guides and help users identify the levels and objects they are interested in. A novel significance measure of dimensions is proposed based on the distribution of IR relevance of cells. During each interaction stage, dimensions are ranked according to their significance scores to guide drilling down; and cells in the same cuboids are ranked according to their relevance to guide exploration. We propose efficient algorithms and materialization strategies for ranking top-k dimensions and cells. Finally, extensive experiments on real datasets demonstrate the efficiency and effectiveness of our approach.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"26 1","pages":"1709-1718"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80297647","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}
With hundreds of millions of participants, social media services have become commonplace. Unlike a traditional social network service, a microblogging network like Twitter is a hybrid network, combining aspects of both social networks and information networks. Understanding the structure of such hybrid networks and predicting new links are important for many tasks such as friend recommendation, community detection, and modeling network growth. We note that the link prediction problem in a hybrid network is different from previously studied networks. Unlike the information networks and traditional online social networks, the structures in a hybrid network are more complicated and informative. We compare most popular and recent methods and principles for link prediction and recommendation. Finally we propose a novel structure-based personalized link prediction model and compare its predictive performance against many fundamental and popular link prediction methods on real-world data from the Twitter microblogging network. Our experiments on both static and dynamic data sets show that our methods noticeably outperform the state-of-the-art.
{"title":"Structural link analysis and prediction in microblogs","authors":"Dawei Yin, Liangjie Hong, Brian D. Davison","doi":"10.1145/2063576.2063743","DOIUrl":"https://doi.org/10.1145/2063576.2063743","url":null,"abstract":"With hundreds of millions of participants, social media services have become commonplace. Unlike a traditional social network service, a microblogging network like Twitter is a hybrid network, combining aspects of both social networks and information networks. Understanding the structure of such hybrid networks and predicting new links are important for many tasks such as friend recommendation, community detection, and modeling network growth. We note that the link prediction problem in a hybrid network is different from previously studied networks. Unlike the information networks and traditional online social networks, the structures in a hybrid network are more complicated and informative. We compare most popular and recent methods and principles for link prediction and recommendation. Finally we propose a novel structure-based personalized link prediction model and compare its predictive performance against many fundamental and popular link prediction methods on real-world data from the Twitter microblogging network. Our experiments on both static and dynamic data sets show that our methods noticeably outperform the state-of-the-art.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"3 1","pages":"1163-1168"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84954873","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}
Peng Li, Bin Wang, Wei Jin, Jian-Yun Nie, Zhiwei Shi, Ben He
User generated social annotations provide extra information for describing document contents. In this paper, we propose an effective method to model the categorization property of social annotations and explore the potential of combining it with classical language models for improving retrieval performance. Specifically, a novel TR-LDA model is presented to take annotations as an additional source for generating document contents apart from the document itself. We provide strategies for representing and weighting the categorization property and develop an efficient inference algorithm, where space saving is taken into account. Experiments are carried out on synthetic datasets, where documents and queries come from the standard evaluation conference TREC and annotations come from the website Delicious.com. Our results demonstrate the effectiveness of the proposed method on the ad-hoc retrieval task, which significantly outperforms state-of-art baselines.
{"title":"Exploring categorization property of social annotations for information retrieval","authors":"Peng Li, Bin Wang, Wei Jin, Jian-Yun Nie, Zhiwei Shi, Ben He","doi":"10.1145/2063576.2063659","DOIUrl":"https://doi.org/10.1145/2063576.2063659","url":null,"abstract":"User generated social annotations provide extra information for describing document contents. In this paper, we propose an effective method to model the categorization property of social annotations and explore the potential of combining it with classical language models for improving retrieval performance. Specifically, a novel TR-LDA model is presented to take annotations as an additional source for generating document contents apart from the document itself. We provide strategies for representing and weighting the categorization property and develop an efficient inference algorithm, where space saving is taken into account. Experiments are carried out on synthetic datasets, where documents and queries come from the standard evaluation conference TREC and annotations come from the website Delicious.com. Our results demonstrate the effectiveness of the proposed method on the ad-hoc retrieval task, which significantly outperforms state-of-art baselines.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"1 1","pages":"557-562"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77052226","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}
Jiyin He, M. de Rijke, M. Sevenster, R. V. Ommering, Y. Qian
Automatically annotating texts with background information has recently received much attention. We conduct a case study in automatically generating links from narrative radiology reports to Wikipedia. Such links help users understand the medical terminology and thereby increase the value of the reports. Direct applications of existing automatic link generation systems trained on Wikipedia to our radiology data do not yield satisfactory results. Our analysis reveals that medical phrases are often syntactically regular but semantically complicated, e.g., containing multiple concepts or concepts with multiple modifiers. The latter property is the main reason for the failure of existing systems. Based on this observation, we propose an automatic link generation approach that takes into account these properties. We use a sequential labeling approach with syntactic features for anchor text identification in order to exploit syntactic regularities in medical terminology. We combine this with a sub-anchor based approach to target finding, which is aimed at coping with the complex semantic structure of medical phrases. Empirical results show that the proposed system effectively improves the performance over existing systems.
{"title":"Generating links to background knowledge: a case study using narrative radiology reports","authors":"Jiyin He, M. de Rijke, M. Sevenster, R. V. Ommering, Y. Qian","doi":"10.1145/2063576.2063845","DOIUrl":"https://doi.org/10.1145/2063576.2063845","url":null,"abstract":"Automatically annotating texts with background information has recently received much attention. We conduct a case study in automatically generating links from narrative radiology reports to Wikipedia. Such links help users understand the medical terminology and thereby increase the value of the reports. Direct applications of existing automatic link generation systems trained on Wikipedia to our radiology data do not yield satisfactory results. Our analysis reveals that medical phrases are often syntactically regular but semantically complicated, e.g., containing multiple concepts or concepts with multiple modifiers. The latter property is the main reason for the failure of existing systems. Based on this observation, we propose an automatic link generation approach that takes into account these properties. We use a sequential labeling approach with syntactic features for anchor text identification in order to exploit syntactic regularities in medical terminology. We combine this with a sub-anchor based approach to target finding, which is aimed at coping with the complex semantic structure of medical phrases. Empirical results show that the proposed system effectively improves the performance over existing systems.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"96 1","pages":"1867-1876"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80961304","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}
Axiom pinpointing plays an important role in the development and maintenance of ontologies. It helps the user to comprehend an unwanted entailment of an ontology by presenting all minimal subsets of the ontology which are responsible for the entailment (called MinAs). In this paper, we consider the problem of axiom pinpointing in description logic EL+, which underpins OWL 2 EL, a profile of the latest version of Web Ontology Language (OWL). We propose a novel method to compute all MinAs that utilizes the hierarchy information obtained from the classification of an EL+ ontology. The advantage of our method over an existing labeled classification based method is that we do not attach labels to entailed subsumptions, which can be memory exhaustion for large scale ontologies. We further consider axiom pinpointing in EL+ when ontologies change. An incremental algorithm is given to compute all MinAs by reusing MinAs previously computed.
{"title":"An algorithm for axiom pinpointing in EL+ and its incremental variant","authors":"Xiaojun Cheng, G. Qi","doi":"10.1145/2063576.2063985","DOIUrl":"https://doi.org/10.1145/2063576.2063985","url":null,"abstract":"Axiom pinpointing plays an important role in the development and maintenance of ontologies. It helps the user to comprehend an unwanted entailment of an ontology by presenting all minimal subsets of the ontology which are responsible for the entailment (called MinAs). In this paper, we consider the problem of axiom pinpointing in description logic EL+, which underpins OWL 2 EL, a profile of the latest version of Web Ontology Language (OWL). We propose a novel method to compute all MinAs that utilizes the hierarchy information obtained from the classification of an EL+ ontology. The advantage of our method over an existing labeled classification based method is that we do not attach labels to entailed subsumptions, which can be memory exhaustion for large scale ontologies. We further consider axiom pinpointing in EL+ when ontologies change. An incremental algorithm is given to compute all MinAs by reusing MinAs previously computed.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"5 1","pages":"2433-2436"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84089093","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 primary objective of privacy preservation is to protect an individual's confidential information in released data sets. In recent years, several simulation-based approaches for privacy preservation have been proposed. The idea is to generate a synthetic data set with the constraint that the probability distribution is as close as possible to that of the original set. In this paper, we propose two frameworks for simulation-based privacy preservation of multivariate numerical data. The first framework, called PRIMP (PRivacy preserving by Independent coMPonents), is based on independent component analysis (ICA). It is shown empirically that PRIMP outperforms other simulation-based approaches in terms of Spearman's rank correlation and Kendall's tau correlation. The second approach proposed is a hybrid method that combines PRIMP and Cholesky's decomposition technique. It is shown empirically that the hybrid method preserves the covariance matrix of the original data exactly. The method also resolves the problem of generating good seeds for the Cholesky-based approach. Although the empirical results show that the hybrid approach is not always better than the PRIMP in terms of Spearman's rank correlation and Kendall's tau correlation, in theory, the risk of information leakage under the hybrid approach is much less than that under PRIMP.
隐私保护的主要目的是保护公开数据集中的个人机密信息。近年来，人们提出了几种基于仿真的隐私保护方法。其思想是生成一个综合数据集，其约束条件是概率分布尽可能接近原始数据集。本文提出了两种基于仿真的多元数值数据隐私保护框架。第一个框架称为PRIMP (PRivacy preserving by Independent coMPonents)，基于独立组件分析(ICA)。经验表明，PRIMP在Spearman的秩相关和Kendall的tau相关方面优于其他基于模拟的方法。第二种方法是结合PRIMP和Cholesky分解技术的混合方法。经验表明，混合方法较好地保留了原始数据的协方差矩阵。该方法还解决了基于cholesky的方法产生好的种子的问题。虽然实证结果表明，混合方法在Spearman的秩相关和Kendall的tau相关方面并不总是优于PRIMP方法，但从理论上讲，混合方法下的信息泄露风险远小于PRIMP方法。
{"title":"Privacy preservation by independent component analysis and variance control","authors":"Chih-Ming Hsu, Ming-Syan Chen","doi":"10.1145/2063576.2063709","DOIUrl":"https://doi.org/10.1145/2063576.2063709","url":null,"abstract":"The primary objective of privacy preservation is to protect an individual's confidential information in released data sets. In recent years, several simulation-based approaches for privacy preservation have been proposed. The idea is to generate a synthetic data set with the constraint that the probability distribution is as close as possible to that of the original set. In this paper, we propose two frameworks for simulation-based privacy preservation of multivariate numerical data. The first framework, called PRIMP (PRivacy preserving by Independent coMPonents), is based on independent component analysis (ICA). It is shown empirically that PRIMP outperforms other simulation-based approaches in terms of Spearman's rank correlation and Kendall's tau correlation. The second approach proposed is a hybrid method that combines PRIMP and Cholesky's decomposition technique. It is shown empirically that the hybrid method preserves the covariance matrix of the original data exactly. The method also resolves the problem of generating good seeds for the Cholesky-based approach. Although the empirical results show that the hybrid approach is not always better than the PRIMP in terms of Spearman's rank correlation and Kendall's tau correlation, in theory, the risk of information leakage under the hybrid approach is much less than that under PRIMP.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"9 1","pages":"925-930"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78465717","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}
Named entities are observed in a large portion of web search queries (named entity queries), where each entity can be associated with many different query terms that refer to various aspects of this entity. Organizing these query terms into topics helps understand major search intents about entities and the discovered topics are useful for applications such as query suggestion. Furthermore, we notice that named entities can often be organized into categories and those from the same category share many generic topics. Therefore, working on a category of named entities instead of individual ones helps avoid the problems caused by the sparsity and noise in the data. In this paper, Named Entity Topic Model (NETM) is proposed to discover generic topics for a category of named entities, where the quality of the generic topics is improved through the model design and the parameter initialization. Experiments based on query log data show that NETM discovers high-quality topics and outperforms the state-of-the-art techniques by 12.8% based on F1 measure.
{"title":"Topic modeling for named entity queries","authors":"Xiaobing Xue, Xiaoxin Yin","doi":"10.1145/2063576.2063877","DOIUrl":"https://doi.org/10.1145/2063576.2063877","url":null,"abstract":"Named entities are observed in a large portion of web search queries (named entity queries), where each entity can be associated with many different query terms that refer to various aspects of this entity. Organizing these query terms into topics helps understand major search intents about entities and the discovered topics are useful for applications such as query suggestion. Furthermore, we notice that named entities can often be organized into categories and those from the same category share many generic topics. Therefore, working on a category of named entities instead of individual ones helps avoid the problems caused by the sparsity and noise in the data. In this paper, Named Entity Topic Model (NETM) is proposed to discover generic topics for a category of named entities, where the quality of the generic topics is improved through the model design and the parameter initialization. Experiments based on query log data show that NETM discovers high-quality topics and outperforms the state-of-the-art techniques by 12.8% based on F1 measure.","PeriodicalId":74507,"journal":{"name":"Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management","volume":"19 Suppl 1 1","pages":"2009-2012"},"PeriodicalIF":0.0,"publicationDate":"2011-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78142701","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}
Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management
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