We investigate the use of multiword query features to improve the effectiveness of text-retrieval systems that accept natural-language queries. A relevance feedback process is explained that expands an initial query with single and multiword features. The multiword features are modelled as a set of words appearing within windows of varying sizes. Our experimental results suggest that windows of larger span yield improvements in retrieval over windows of smaller span. This result gives rise to a query contraction process that prunes 25% of the features in an expanded query with no loss in retrieval effectiveness. 1 Introduction The following work investigates the representation for queries used in text-based information retrieval systems. The query representation described has applications in document filtering, routing, and clustering in addition to website searching. Our primary focus is the use of query features that represent concepts expressible in natural language by multiple word...

Article On-line new event detection and tracking Share on Authors: James Allan Center for Intelligent Information Retrieval, Computer Science Department, University of Massachusetts, Amherst, MA Center for Intelligent Information Retrieval, Computer Science Department, University of Massachusetts, Amherst, MAView Profile , Ron Papka Center for Intelligent Information Retrieval, Computer Science Department, University of Massachusetts, Amherst, MA Center for Intelligent Information Retrieval, Computer Science Department, University of Massachusetts, Amherst, MAView Profile , Victor Lavrenko Center for Intelligent Information Retrieval, Computer Science Department, University of Massachusetts, Amherst, MA Center for Intelligent Information Retrieval, Computer Science Department, University of Massachusetts, Amherst, MAView Profile Authors Info & Claims SIGIR '98: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrievalAugust 1998 Pages 37–45https://doi.org/10.1145/290941.290954Online:01 August 1998Publication History 462citation4,318DownloadsMetricsTotal Citations462Total Downloads4,318Last 12 Months146Last 6 weeks18 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Systems for text retrieval, routing, categorization and other IR tasks rely heavily on linear classifiers.We propose that two machine learning algorithms, the Widrow-Hoff and EG algorithms, be used in training linear text classifiers.In contrast to most IR methods, theoretical analysis provides performance guarantees and guidance on parameter settings for these algorithms.Experimental data is presented showing Widrow-Hoff and EG to be more effective than the widely used Rocchio algorithm on several categorization and routing tasks.