Session search is the Information Retrieval (IR) task that performs document retrieval for an entire session. During a session, users often change queries to explore and investigate the information needs. In this paper, we propose to use query change as a new form of relevance feedback for better session search. Evaluation conducted over TREC 2012 Session Track shows that query change is a highly effective form of feedback as compared with existing relevance feedback methods. The proposed method outperforms the state-of-the-art relevance feedback methods for the TREC 2012 Session Track by a significant improvement of >25%.
{"title":"Query change as relevance feedback in session search","authors":"Sicong Zhang, Dongyi Guan, G. Yang","doi":"10.1145/2484028.2484171","DOIUrl":"https://doi.org/10.1145/2484028.2484171","url":null,"abstract":"Session search is the Information Retrieval (IR) task that performs document retrieval for an entire session. During a session, users often change queries to explore and investigate the information needs. In this paper, we propose to use query change as a new form of relevance feedback for better session search. Evaluation conducted over TREC 2012 Session Track shows that query change is a highly effective form of feedback as compared with existing relevance feedback methods. The proposed method outperforms the state-of-the-art relevance feedback methods for the TREC 2012 Session Track by a significant improvement of >25%.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128728221","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}
Parvaz Mahdabi, Shima Gerani, Xiangji Huang, F. Crestani
Patent prior art search is a task in patent retrieval where the goal is to rank documents which describe prior art work related to a patent application. One of the main properties of patent retrieval is that the query topic is a full patent application and does not represent a focused information need. This query by document nature of patent retrieval introduces new challenges and requires new investigations specific to this problem. Researchers have addressed this problem by considering different information resources for query reduction and query disambiguation. However, previous work has not fully studied the effect of using proximity information and exploiting domain specific resources for performing query disambiguation. In this paper, we first reduce the query document by taking the first claim of the document itself. We then build a query-specific patent lexicon based on definitions of the International Patent Classification (IPC). We study how to expand queries by selecting expansion terms from the lexicon that are focused on the query topic. The key problem is how to capture whether an expansion term is focused on the query topic or not. We address this problem by exploiting proximity information. We assign high weights to expansion terms appearing closer to query terms based on the intuition that terms closer to query terms are more likely to be related to the query topic. Experimental results on two patent retrieval datasets show that the proposed method is effective and robust for query expansion, significantly outperforming the standard pseudo relevance feedback (PRF) and existing baselines in patent retrieval.
{"title":"Leveraging conceptual lexicon: query disambiguation using proximity information for patent retrieval","authors":"Parvaz Mahdabi, Shima Gerani, Xiangji Huang, F. Crestani","doi":"10.1145/2484028.2484056","DOIUrl":"https://doi.org/10.1145/2484028.2484056","url":null,"abstract":"Patent prior art search is a task in patent retrieval where the goal is to rank documents which describe prior art work related to a patent application. One of the main properties of patent retrieval is that the query topic is a full patent application and does not represent a focused information need. This query by document nature of patent retrieval introduces new challenges and requires new investigations specific to this problem. Researchers have addressed this problem by considering different information resources for query reduction and query disambiguation. However, previous work has not fully studied the effect of using proximity information and exploiting domain specific resources for performing query disambiguation. In this paper, we first reduce the query document by taking the first claim of the document itself. We then build a query-specific patent lexicon based on definitions of the International Patent Classification (IPC). We study how to expand queries by selecting expansion terms from the lexicon that are focused on the query topic. The key problem is how to capture whether an expansion term is focused on the query topic or not. We address this problem by exploiting proximity information. We assign high weights to expansion terms appearing closer to query terms based on the intuition that terms closer to query terms are more likely to be related to the query topic. Experimental results on two patent retrieval datasets show that the proposed method is effective and robust for query expansion, significantly outperforming the standard pseudo relevance feedback (PRF) and existing baselines in patent retrieval.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"55 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115926600","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 engines can improve their efficiency by selecting only few promising shards for each query. State-of-the-art shard selection algorithms first query a central index of sampled documents, and their effectiveness is similar to searching all shards. However, the search in the central index also hurts efficiency. Additionally, we show that the effectiveness of these approaches varies substantially with the sampled documents. This paper proposes Taily, a novel shard selection algorithm that models a query's score distribution in each shard as a Gamma distribution and selects shards with highly scored documents in the tail of the distribution. Taily estimates the parameters of score distributions based on the mean and variance of the score function's features in the collections and shards. Because Taily operates on term statistics instead of document samples, it is efficient and has deterministic effectiveness. Experiments on large web collections (Gov2, CluewebA and CluewebB) show that Taily achieves similar effectiveness to sample-based approaches, and improves upon their efficiency by roughly 20% in terms of used resources and response time.
{"title":"Taily","authors":"Robin Aly, D. Hiemstra, T. Demeester","doi":"10.1145/2484028.2484033","DOIUrl":"https://doi.org/10.1145/2484028.2484033","url":null,"abstract":"Search engines can improve their efficiency by selecting only few promising shards for each query. State-of-the-art shard selection algorithms first query a central index of sampled documents, and their effectiveness is similar to searching all shards. However, the search in the central index also hurts efficiency. Additionally, we show that the effectiveness of these approaches varies substantially with the sampled documents. This paper proposes Taily, a novel shard selection algorithm that models a query's score distribution in each shard as a Gamma distribution and selects shards with highly scored documents in the tail of the distribution. Taily estimates the parameters of score distributions based on the mean and variance of the score function's features in the collections and shards. Because Taily operates on term statistics instead of document samples, it is efficient and has deterministic effectiveness. Experiments on large web collections (Gov2, CluewebA and CluewebB) show that Taily achieves similar effectiveness to sample-based approaches, and improves upon their efficiency by roughly 20% in terms of used resources and response time.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116028079","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}
Pub Date : 2013-07-28DOI: 10.1007/978-3-319-06028-6_105
Oren Kurland
{"title":"The cluster hypothesis in information retrieval","authors":"Oren Kurland","doi":"10.1007/978-3-319-06028-6_105","DOIUrl":"https://doi.org/10.1007/978-3-319-06028-6_105","url":null,"abstract":"","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116745478","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}
Document expansion (DE) in information retrieval (IR) involves modifying each document in the collection by introducing additional terms into the document. It is particularly useful to improve retrieval of short and noisy documents where the additional terms can improve the description of the document content. Existing approaches to DE assume that documents to be expanded are from a single topic. In the case of multi-topic documents this can lead to a topic bias in terms selected for DE and hence may result in poor retrieval quality due to the lack of coverage of the original document topics in the expanded document. This paper proposes a new DE technique providing a more uniform selection and weighting of DE terms from all constituent topics. We show that our proposed method significantly outperforms the most recently reported relevance model based DE method on a spoken document retrieval task for both manual and automatic speech recognition transcripts.
{"title":"An LDA-smoothed relevance model for document expansion: a case study for spoken document retrieval","authors":"Debasis Ganguly, Johannes Leveling, G. Jones","doi":"10.1145/2484028.2484110","DOIUrl":"https://doi.org/10.1145/2484028.2484110","url":null,"abstract":"Document expansion (DE) in information retrieval (IR) involves modifying each document in the collection by introducing additional terms into the document. It is particularly useful to improve retrieval of short and noisy documents where the additional terms can improve the description of the document content. Existing approaches to DE assume that documents to be expanded are from a single topic. In the case of multi-topic documents this can lead to a topic bias in terms selected for DE and hence may result in poor retrieval quality due to the lack of coverage of the original document topics in the expanded document. This paper proposes a new DE technique providing a more uniform selection and weighting of DE terms from all constituent topics. We show that our proposed method significantly outperforms the most recently reported relevance model based DE method on a spoken document retrieval task for both manual and automatic speech recognition transcripts.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115578548","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 develop and make publicly available an entity search test collection based on the DBpedia knowledge base. This includes a large number of queries and corresponding relevance judgments from previous benchmarking campaigns, covering a broad range of information needs, ranging from short keyword queries to natural language questions. Further, we present baseline results for this collection with a set of retrieval models based on language modeling and BM25. Finally, we perform an initial analysis to shed light on certain characteristics that make this data set particularly challenging.
{"title":"A test collection for entity search in DBpedia","authors":"K. Balog, R. Neumayer","doi":"10.1145/2484028.2484165","DOIUrl":"https://doi.org/10.1145/2484028.2484165","url":null,"abstract":"We develop and make publicly available an entity search test collection based on the DBpedia knowledge base. This includes a large number of queries and corresponding relevance judgments from previous benchmarking campaigns, covering a broad range of information needs, ranging from short keyword queries to natural language questions. Further, we present baseline results for this collection with a set of retrieval models based on language modeling and BM25. Finally, we perform an initial analysis to shed light on certain characteristics that make this data set particularly challenging.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114337998","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}
Swapnil Hingmire, S. Chougule, Girish Keshav Palshikar, Sutanu Chakraborti
In this paper, we propose Latent Dirichlet Allocation (LDA) [1] based document classification algorithm which does not require any labeled dataset. In our algorithm, we construct a topic model using LDA, assign one topic to one of the class labels, aggregate all the same class label topics into a single topic using the aggregation property of the Dirichlet distribution and then automatically assign a class label to each unlabeled document depending on its "closeness" to one of the aggregated topics. We present an extension to our algorithm based on the combination of Expectation-Maximization (EM) algorithm and a naive Bayes classifier. We show effectiveness of our algorithm on three real world datasets.
{"title":"Document classification by topic labeling","authors":"Swapnil Hingmire, S. Chougule, Girish Keshav Palshikar, Sutanu Chakraborti","doi":"10.1145/2484028.2484140","DOIUrl":"https://doi.org/10.1145/2484028.2484140","url":null,"abstract":"In this paper, we propose Latent Dirichlet Allocation (LDA) [1] based document classification algorithm which does not require any labeled dataset. In our algorithm, we construct a topic model using LDA, assign one topic to one of the class labels, aggregate all the same class label topics into a single topic using the aggregation property of the Dirichlet distribution and then automatically assign a class label to each unlabeled document depending on its \"closeness\" to one of the aggregated topics. We present an extension to our algorithm based on the combination of Expectation-Maximization (EM) algorithm and a naive Bayes classifier. We show effectiveness of our algorithm on three real world datasets.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114621196","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}
Collaborative filtering techniques rely on aggregated user preference data to make personalized predictions. In many cases, users are reluctant to explicitly express their preferences and many recommender systems have to infer them from implicit user behaviors, such as clicking a link in a webpage or playing a music track. The clicks and the plays are good for indicating the items a user liked (i.e., positive training examples), but the items a user did not like (negative training examples) are not directly observed. Previous approaches either randomly pick negative training samples from unseen items or incorporate some heuristics into the learning model, leading to a biased solution and a prolonged training period. In this paper, we propose to dynamically choose negative training samples from the ranked list produced by the current prediction model and iteratively update our model. The experiments conducted on three large-scale datasets show that our approach not only reduces the training time, but also leads to significant performance gains.
{"title":"Optimizing top-n collaborative filtering via dynamic negative item sampling","authors":"Weinan Zhang, Tianqi Chen, Jun Wang, Yong Yu","doi":"10.1145/2484028.2484126","DOIUrl":"https://doi.org/10.1145/2484028.2484126","url":null,"abstract":"Collaborative filtering techniques rely on aggregated user preference data to make personalized predictions. In many cases, users are reluctant to explicitly express their preferences and many recommender systems have to infer them from implicit user behaviors, such as clicking a link in a webpage or playing a music track. The clicks and the plays are good for indicating the items a user liked (i.e., positive training examples), but the items a user did not like (negative training examples) are not directly observed. Previous approaches either randomly pick negative training samples from unseen items or incorporate some heuristics into the learning model, leading to a biased solution and a prolonged training period. In this paper, we propose to dynamically choose negative training samples from the ranked list produced by the current prediction model and iteratively update our model. The experiments conducted on three large-scale datasets show that our approach not only reduces the training time, but also leads to significant performance gains.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114649751","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}
Alexey Tikhonov, Ivan Bogatyy, Pavel Burangulov, L. Ostroumova, V. Koshelev, Gleb Gusev
With the ever-increasing speed of content turnover on the web, it is particularly important to understand the patterns that pages' popularity follows. This paper focuses on the dynamical part of the web, i.e. pages that have a limited lifespan and experience a short popularity outburst within it. We classify these pages into five patterns based on how quickly they gain popularity and how quickly they lose it. We study the properties of pages that belong to each pattern and determine content topics that contain disproportionately high fractions of particular patterns. These developments are utilized to create an algorithm that approximates with reasonable accuracy the expected popularity pattern of a web page based on its URL and, if available, prior knowledge about its domain's topics.
{"title":"Studying page life patterns in dynamical web","authors":"Alexey Tikhonov, Ivan Bogatyy, Pavel Burangulov, L. Ostroumova, V. Koshelev, Gleb Gusev","doi":"10.1145/2484028.2484185","DOIUrl":"https://doi.org/10.1145/2484028.2484185","url":null,"abstract":"With the ever-increasing speed of content turnover on the web, it is particularly important to understand the patterns that pages' popularity follows. This paper focuses on the dynamical part of the web, i.e. pages that have a limited lifespan and experience a short popularity outburst within it. We classify these pages into five patterns based on how quickly they gain popularity and how quickly they lose it. We study the properties of pages that belong to each pattern and determine content topics that contain disproportionately high fractions of particular patterns. These developments are utilized to create an algorithm that approximates with reasonable accuracy the expected popularity pattern of a web page based on its URL and, if available, prior knowledge about its domain's topics.","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126847431","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}
Diego Arroyuelo, Senén González, M. Oyarzún, Victor Sepulveda
Text search engines are a fundamental tool nowadays. Their efficiency relies on a popular and simple data structure: the inverted indexes. Currently, inverted indexes can be represented very efficiently using index compression schemes. Recent investigations also study how an optimized document ordering can be used to assign document identifiers (docIDs) to the document database. This yields important improvements in index compression and query processing time. In this paper we follow this line of research, yet from a different perspective. We propose a docID reassignment method that allows one to focus on a given subset of inverted lists to improve their performance. We then use run-length encoding to compress these lists (as many consecutive 1s are generated). We show that by using this approach, not only the performance of the particular subset of inverted lists is improved, but also that of the whole inverted index. Our experimental results indicate a reduction of about 10% in the space usage of the whole index docID reassignment was focused. Also, decompression speed is up to 1.22 times faster if the runs must be explicitly decompressed and up to 4.58 times faster if implicit decompression of runs is allowed. Finally, we also improve the Document-at-a-Time query processing time of AND queries (by up to 12%), WAND queries (by up to 23%) and full (non-ranked) OR queries (by up to 86%).
{"title":"Document identifier reassignment and run-length-compressed inverted indexes for improved search performance","authors":"Diego Arroyuelo, Senén González, M. Oyarzún, Victor Sepulveda","doi":"10.1145/2484028.2484079","DOIUrl":"https://doi.org/10.1145/2484028.2484079","url":null,"abstract":"Text search engines are a fundamental tool nowadays. Their efficiency relies on a popular and simple data structure: the inverted indexes. Currently, inverted indexes can be represented very efficiently using index compression schemes. Recent investigations also study how an optimized document ordering can be used to assign document identifiers (docIDs) to the document database. This yields important improvements in index compression and query processing time. In this paper we follow this line of research, yet from a different perspective. We propose a docID reassignment method that allows one to focus on a given subset of inverted lists to improve their performance. We then use run-length encoding to compress these lists (as many consecutive 1s are generated). We show that by using this approach, not only the performance of the particular subset of inverted lists is improved, but also that of the whole inverted index. Our experimental results indicate a reduction of about 10% in the space usage of the whole index docID reassignment was focused. Also, decompression speed is up to 1.22 times faster if the runs must be explicitly decompressed and up to 4.58 times faster if implicit decompression of runs is allowed. Finally, we also improve the Document-at-a-Time query processing time of AND queries (by up to 12%), WAND queries (by up to 23%) and full (non-ranked) OR queries (by up to 86%).","PeriodicalId":178818,"journal":{"name":"Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121910386","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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