The Experts below are selected from a list of 13029 Experts worldwide ranked by ideXlab platform
Jian-yun Nie - One of the best experts on this subject based on the ideXlab platform.
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Query Expansion and Query translation as logical inference
Journal of the American Society for Information Science and Technology, 2003Co-Authors: Jian-yun NieAbstract:A number of studies have examined the problems of Query Expansion in monolingual Information Retrieval (IR), and Query translation for crosslanguage IR. However, no link has been made between them. This article first shows that Query translation is a special case of Query Expansion. There is also another set of studies on inferential IR. Again, there is no relationship established with Query translation or Query Expansion. The second claim of this article is that logical inference is a general form that covers Query Expansion and Query translation. This analysis provides a unified view of different subareas of IR. We further develop the inferential IR approach in two particular contexts: using fuzzy logic and probability theory. The evaluation formulas obtained are shown to strongly correspond to those used in other IR models. This indicates that inference is indeed the core of advanced IR.
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Query Expansion by mining user logs
IEEE Transactions on Knowledge and Data Engineering, 2003Co-Authors: Hang Cui, Ji-rong Wen, Jian-yun NieAbstract:Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of Query Expansion to deal with this problem. However, Expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for Query Expansion based on user interactions recorded in user logs. The central idea is to extract correlations between Query terms and document terms by analyzing user logs. These correlations are then used to select high-quality Expansion terms for new queries. Compared to previous Query Expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based Query Expansion method can produce much better results than both the classical search method and the other Query Expansion methods.
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probabilistic Query Expansion using Query logs
The Web Conference, 2002Co-Authors: Hang Cui, Ji-rong Wen, Jian-yun NieAbstract:Query Expansion has long been suggested as an effective way to resolve the short Query and word mismatching problems. A number of Query Expansion methods have been proposed in traditional information retrieval. However, these previous methods do not take into account the specific characteristics of web searching; in particular, of the availability of large amount of user interaction information recorded in the web Query logs. In this study, we propose a new method for Query Expansion based on Query logs. The central idea is to extract probabilistic correlations between Query terms and document terms by analyzing Query logs. These correlations are then used to select high-quality Expansion terms for new queries. The experimental results show that our log-based probabilistic Query Expansion method can greatly improve the search performance and has several advantages over other existing methods.
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WWW - Probabilistic Query Expansion using Query logs
Proceedings of the eleventh international conference on World Wide Web - WWW '02, 2002Co-Authors: Hang Cui, Ji-rong Wen, Jian-yun NieAbstract:Query Expansion has long been suggested as an effective way to resolve the short Query and word mismatching problems. A number of Query Expansion methods have been proposed in traditional information retrieval. However, these previous methods do not take into account the specific characteristics of web searching; in particular, of the availability of large amount of user interaction information recorded in the web Query logs. In this study, we propose a new method for Query Expansion based on Query logs. The central idea is to extract probabilistic correlations between Query terms and document terms by analyzing Query logs. These correlations are then used to select high-quality Expansion terms for new queries. The experimental results show that our log-based probabilistic Query Expansion method can greatly improve the search performance and has several advantages over other existing methods.
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Query Expansion for Short Queries by Mining User Logs
2002Co-Authors: Hang Cui, Ji-rong Wen, Jian-yun NieAbstract:Queries to search engines on the Web are usually short. They do not provide sufficient indications for an effective selection of relevant documents. Previous research has proposed the utilization of Query Expansion to deal with this problem. However, Expansion terms are determined only on the analysis of documents. In this study, we propose a new method for Query Expansion based on user interaction information recorded in the web Query logs. The central idea is to extract correlations between Query terms and document terms by analyzing Query logs. These correlations are then used to select high-quality Expansion terms for new queries. In comparison with previous Query Expansion method, our method takes advantage of the user judgments implied in user logs. Our experimental results show that the log-based Query Expansion method can produce much better results than both the classical search method and the other Query Expansion methods.
Hang Cui - One of the best experts on this subject based on the ideXlab platform.
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Query Expansion by mining user logs
IEEE Transactions on Knowledge and Data Engineering, 2003Co-Authors: Hang Cui, Ji-rong Wen, Jian-yun NieAbstract:Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of Query Expansion to deal with this problem. However, Expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for Query Expansion based on user interactions recorded in user logs. The central idea is to extract correlations between Query terms and document terms by analyzing user logs. These correlations are then used to select high-quality Expansion terms for new queries. Compared to previous Query Expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based Query Expansion method can produce much better results than both the classical search method and the other Query Expansion methods.
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probabilistic Query Expansion using Query logs
The Web Conference, 2002Co-Authors: Hang Cui, Ji-rong Wen, Jian-yun NieAbstract:Query Expansion has long been suggested as an effective way to resolve the short Query and word mismatching problems. A number of Query Expansion methods have been proposed in traditional information retrieval. However, these previous methods do not take into account the specific characteristics of web searching; in particular, of the availability of large amount of user interaction information recorded in the web Query logs. In this study, we propose a new method for Query Expansion based on Query logs. The central idea is to extract probabilistic correlations between Query terms and document terms by analyzing Query logs. These correlations are then used to select high-quality Expansion terms for new queries. The experimental results show that our log-based probabilistic Query Expansion method can greatly improve the search performance and has several advantages over other existing methods.
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WWW - Probabilistic Query Expansion using Query logs
Proceedings of the eleventh international conference on World Wide Web - WWW '02, 2002Co-Authors: Hang Cui, Ji-rong Wen, Jian-yun NieAbstract:Query Expansion has long been suggested as an effective way to resolve the short Query and word mismatching problems. A number of Query Expansion methods have been proposed in traditional information retrieval. However, these previous methods do not take into account the specific characteristics of web searching; in particular, of the availability of large amount of user interaction information recorded in the web Query logs. In this study, we propose a new method for Query Expansion based on Query logs. The central idea is to extract probabilistic correlations between Query terms and document terms by analyzing Query logs. These correlations are then used to select high-quality Expansion terms for new queries. The experimental results show that our log-based probabilistic Query Expansion method can greatly improve the search performance and has several advantages over other existing methods.
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Query Expansion for Short Queries by Mining User Logs
2002Co-Authors: Hang Cui, Ji-rong Wen, Jian-yun NieAbstract:Queries to search engines on the Web are usually short. They do not provide sufficient indications for an effective selection of relevant documents. Previous research has proposed the utilization of Query Expansion to deal with this problem. However, Expansion terms are determined only on the analysis of documents. In this study, we propose a new method for Query Expansion based on user interaction information recorded in the web Query logs. The central idea is to extract correlations between Query terms and document terms by analyzing Query logs. These correlations are then used to select high-quality Expansion terms for new queries. In comparison with previous Query Expansion method, our method takes advantage of the user judgments implied in user logs. Our experimental results show that the log-based Query Expansion method can produce much better results than both the classical search method and the other Query Expansion methods.
Ji-rong Wen - One of the best experts on this subject based on the ideXlab platform.
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Query Expansion by mining user logs
IEEE Transactions on Knowledge and Data Engineering, 2003Co-Authors: Hang Cui, Ji-rong Wen, Jian-yun NieAbstract:Queries to search engines on the Web are usually short. They do not provide sufficient information for an effective selection of relevant documents. Previous research has proposed the utilization of Query Expansion to deal with this problem. However, Expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for Query Expansion based on user interactions recorded in user logs. The central idea is to extract correlations between Query terms and document terms by analyzing user logs. These correlations are then used to select high-quality Expansion terms for new queries. Compared to previous Query Expansion methods, ours takes advantage of the user judgments implied in user logs. The experimental results show that the log-based Query Expansion method can produce much better results than both the classical search method and the other Query Expansion methods.
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probabilistic Query Expansion using Query logs
The Web Conference, 2002Co-Authors: Hang Cui, Ji-rong Wen, Jian-yun NieAbstract:Query Expansion has long been suggested as an effective way to resolve the short Query and word mismatching problems. A number of Query Expansion methods have been proposed in traditional information retrieval. However, these previous methods do not take into account the specific characteristics of web searching; in particular, of the availability of large amount of user interaction information recorded in the web Query logs. In this study, we propose a new method for Query Expansion based on Query logs. The central idea is to extract probabilistic correlations between Query terms and document terms by analyzing Query logs. These correlations are then used to select high-quality Expansion terms for new queries. The experimental results show that our log-based probabilistic Query Expansion method can greatly improve the search performance and has several advantages over other existing methods.
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WWW - Probabilistic Query Expansion using Query logs
Proceedings of the eleventh international conference on World Wide Web - WWW '02, 2002Co-Authors: Hang Cui, Ji-rong Wen, Jian-yun NieAbstract:Query Expansion has long been suggested as an effective way to resolve the short Query and word mismatching problems. A number of Query Expansion methods have been proposed in traditional information retrieval. However, these previous methods do not take into account the specific characteristics of web searching; in particular, of the availability of large amount of user interaction information recorded in the web Query logs. In this study, we propose a new method for Query Expansion based on Query logs. The central idea is to extract probabilistic correlations between Query terms and document terms by analyzing Query logs. These correlations are then used to select high-quality Expansion terms for new queries. The experimental results show that our log-based probabilistic Query Expansion method can greatly improve the search performance and has several advantages over other existing methods.
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Query Expansion for Short Queries by Mining User Logs
2002Co-Authors: Hang Cui, Ji-rong Wen, Jian-yun NieAbstract:Queries to search engines on the Web are usually short. They do not provide sufficient indications for an effective selection of relevant documents. Previous research has proposed the utilization of Query Expansion to deal with this problem. However, Expansion terms are determined only on the analysis of documents. In this study, we propose a new method for Query Expansion based on user interaction information recorded in the web Query logs. The central idea is to extract correlations between Query terms and document terms by analyzing Query logs. These correlations are then used to select high-quality Expansion terms for new queries. In comparison with previous Query Expansion method, our method takes advantage of the user judgments implied in user logs. Our experimental results show that the log-based Query Expansion method can produce much better results than both the classical search method and the other Query Expansion methods.
Le Gruenwald - One of the best experts on this subject based on the ideXlab platform.
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Query Expansion using web access log files
Lecture Notes in Computer Science, 2005Co-Authors: Yun Zhu, Le GruenwaldAbstract:Query Expansion has long been recognized as one of the effective methods in solving short queries and improving ranking accuracy in traditional IR research. Many variations of this method have been introduced throughout the past decades; however, few of them have incorporated web log information into the Query Expansion process. In this paper, we propose an Expansion technique that expands document content at the initial index stage using queries extracted from the web log files. Our experimental results show that even with a minimal amount of real world log information available and a professionally cataloged knowledge structure to aid the search, there is still a significant improvement in using our Query Expansion method compared to the conventional Query Expansion ones.
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DEXA - Query Expansion using web access log files
Lecture Notes in Computer Science, 2005Co-Authors: Yun Zhu, Le GruenwaldAbstract:Query Expansion has long been recognized as one of the effective methods in solving short queries and improving ranking accuracy in traditional IR research. Many variations of this method have been introduced throughout the past decades; however, few of them have incorporated web log information into the Query Expansion process. In this paper, we propose an Expansion technique that expands document content at the initial index stage using queries extracted from the web log files. Our experimental results show that even with a minimal amount of real world log information available and a professionally cataloged knowledge structure to aid the search, there is still a significant improvement in using our Query Expansion method compared to the conventional Query Expansion ones.
Hanspeter Frei - One of the best experts on this subject based on the ideXlab platform.
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concept based Query Expansion
International ACM SIGIR Conference on Research and Development in Information Retrieval, 1993Co-Authors: Yonggang Qiu, Hanspeter FreiAbstract:Query Expansion methods have been studied for a long time - with debatable success in many instances. In this paper we present a probabilistic Query Expansion model based on a similarity thesaurus which was constructed automatically. A similarity thesaurus reflects domain knowledge about the particular collection from which it is constructed. We address the two important issues with Query Expansion: the selection and the weighting of additional search terms. In contrast to earlier methods, our queries are expanded by adding those terms that are most similar to the concept of the Query, rather than selecting terms that are similar to the Query terms. Our experiments show that this kind of Query Expansion results in a notable improvement in the retrieval effectiveness when measured using both recall-precision and usefulness.
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SIGIR - Concept based Query Expansion
Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '93, 1993Co-Authors: Yonggang Qiu, Hanspeter FreiAbstract:Query Expansion methods have been studied for a long time - with debatable success in many instances. In this paper we present a probabilistic Query Expansion model based on a similarity thesaurus which was constructed automatically. A similarity thesaurus reflects domain knowledge about the particular collection from which it is constructed. We address the two important issues with Query Expansion: the selection and the weighting of additional search terms. In contrast to earlier methods, our queries are expanded by adding those terms that are most similar to the concept of the Query, rather than selecting terms that are similar to the Query terms. Our experiments show that this kind of Query Expansion results in a notable improvement in the retrieval effectiveness when measured using both recall-precision and usefulness.
