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Ranking (information retrieval)

About: Ranking (information retrieval) is a research topic. Over the lifetime, 21109 publications have been published within this topic receiving 435130 citations.


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Patent
Eric D. Brill1, Jesper B. Lind1, Marc A. Smith1, Wensi Xi1, Duncan L. Davenport1 
08 Apr 2004
TL;DR: In this paper, the authors present systems and methods that rank search results by determining a relevance of individual search results via one or more feature-based relevance functions, which can be tailored to users and applications, and typically are based on scoped information (e.g., lexical), digital artifact author related attributes, digital artifact source repository attributes, and/or relationships between features.
Abstract: The present invention provides systems and methods that rank search results. Such ranking typically includes determining a relevance of individual search results via one or more feature-based relevance functions. These functions can be tailored to users and/or applications, and typically are based on scoped information (e.g., lexical), digital artifact author related attributes, digital artifact source repository attributes, and/or relationships between features, for example. In addition, relevance functions can be generated via training sets (e.g., machine learning) or initial guesses that are iteratively refined over time. Upon determining relevance, search results can be ordered with respect to one another, based on respective relevances. Additionally, thresholding can be utilized to mitigate returning results likely to be non-relevant to the query, user and/or application.

124 citations

Patent
Andrew Hogue1
24 Mar 2006
TL;DR: In this article, a method and a system for providing snippets of source documents of an answer to a fact query are disclosed, along with Uniform Resource Locators (URL's) of the source documents.
Abstract: A method and a system for providing snippets of source documents of an answer to a fact query are disclosed. Snippets of source documents may be provided in response to a user request for the source documents from which the fact answer to a fact query was extracted. The snippets include the terms of the fact query and terms of the answer. The snippets may be displayed along with Uniform Resource Locators (URL's) of the source documents.

124 citations

Proceedings ArticleDOI
Hwanjo Yu1
21 Aug 2005
TL;DR: The proposed sampling technique effectively learns an accurate SVM ranking function with fewer partial orders, and is applied to the data retrieval application, which enables fuzzy search on relational databases by interacting with users for learning their preferences.
Abstract: Learning ranking (or preference) functions has been a major issue in the machine learning community and has produced many applications in information retrieval. SVMs (Support Vector Machines) - a classification and regression methodology - have also shown excellent performance in learning ranking functions. They effectively learn ranking functions of high generalization based on the "large-margin" principle and also systematically support nonlinear ranking by the "kernel trick". In this paper, we propose an SVM selective sampling technique for learning ranking functions. SVM selective sampling (or active learning with SVM) has been studied in the context of classification. Such techniques reduce the labeling effort in learning classification functions by selecting only the most informative samples to be labeled. However, they are not extendable to learning ranking functions, as the labeled data in ranking is relative ordering, or partial orders of data. Our proposed sampling technique effectively learns an accurate SVM ranking function with fewer partial orders. We apply our sampling technique to the data retrieval application, which enables fuzzy search on relational databases by interacting with users for learning their preferences. Experimental results show a significant reduction of the labeling effort in inducing accurate ranking functions.

124 citations

Journal ArticleDOI
Candy Schwartz1
TL;DR: The shift to distributed search across multitype database systems could extend general networked discovery and retrieval to include smaller resource collections with rich metadata and navigation tools.
Abstract: This review looks briefly at the history of World Wide Web search engine development, considers the current state of affairs, and reflects on the future. Networked discovery tools have evolved along with Internet resource availability. World Wide Web search engines display some complexity in their variety, content, resource acquisition strategies, and in the array of tools they deploy to assist users. A small but growing body of evaluation literature, much of it not systematic in nature, indicates that performance effectiveness is difficult to assess in this setting. Significant improvements in general-content search engine retrieval and ranking performance may not be possible, and are probably not worth the effort, although search engine providers have introduced some rudimentary attempts at personalization, summarization, and query expansion. The shift to distributed search across multitype database systems could extend general networked discovery and retrieval to include smaller resource collections with rich metadata and navigation tools. © 1998 John Wiley & Sons, Inc.

124 citations

Proceedings ArticleDOI
09 Dec 2008
TL;DR: This research automatically classify documents into reader-emotion categories, and examines classification performance under different feature settings, showing that certain feature combinations achieve good accuracy.
Abstract: Past studies on emotion classification focus on the writerpsilas emotional state. This research addresses the reader aspect instead. The classification of documents into reader-emotion categories has several applications. One of them is to integrate reader-emotion classification into a Web search engine to allow users to retrieve documents that contain relevant contents and at the same time instill proper emotions. In this paper, we automatically classify documents into reader-emotion categories, and examine classification performance under different feature settings. Experiments show that certain feature combinations achieve good accuracy. We also compare the best classifierpsilas classification results with the emotional distributions of documents to determine how closely the classifier models the underlying reader behavior. Finally, we investigate the feasibility of emotion ranking.

124 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20233,112
20226,541
20211,105
20201,082
20191,168