We present findings from a log based study designed to track the adoption of features of a new real-time query refinement interface deployed on the Yahoo search engine. Several trends from the first four months are noted and discussed.

Although interactive query reformulation has been actively studied in the laboratory, little is known about the actual behavior of web searchers who are offered terminological feedback along with their search results. We analyze log sessions for two groups of users interacting with variants of the AltaVista search engine - a baseline group given no terminological feedback and a feedback group to whom twelve refinement terms are offered along with the search results. We examine uptake, refinement effectiveness, conditions of use, and refinement type preferences. Although our measure of overall session success shows no difference between outcomes for the two groups, we find evidence that a subset of those users presented with terminological feedback do make effective use of it on a continuing basis.

We present a new linguistic approach to the construction of terminological feedback for use in interactive query refinement.The method exploits the tendency for key domain concepts within result sets to participate in families of semantically related lexical compounds.We outline an algorithm for computing a ranked list of result set "themes" and describe a web application, the Paraphrase Search Assistant, designed to make use of the theme extraction algorithm to support a recognition-based, iterative information seeking dialog.

article Free Access Share on Exploiting clustering and phrases for context-based information retrieval Authors: Peter G. Anick Digital Equipment Corporation, Stow, MA and Brandeis University, Waltham, MA Digital Equipment Corporation, Stow, MA and Brandeis University, Waltham, MAView Profile , Shivakumar Vaithyanathan IBM Almaden Research Center, San Jose, CA IBM Almaden Research Center, San Jose, CAView Profile Authors Info & Claims ACM SIGIR ForumVolume 31Issue SIDecember 1997pp 314–323https://doi.org/10.1145/278459.258601Published:01 July 1997Publication History 68citation1,735DownloadsMetricsTotal Citations68Total Downloads1,735Last 12 Months71Last 6 weeks13 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 SiteeReaderPDF

There has been a great deal of interest within the Information Retrieval community in evaluating the use of linguistic knowledge to improve the indexing and searching of textual databases. Such systems must often employ a lexicon to store information about the words and phrases comprising the application's domain. Unlike a static lexicon, a dynamic lexicon raises practical concerns about the coordination between the state of the lexicon and IR indexing schemes based on lexical knowledge. Additionally, it introduces a host of database management issues, many of which are similar to those found in the text databases as well. In this paper, we explore a range of system design and performance issues that arise when integrating a dynamic lexicon with a dynamic full-text information retrieval system. We observe that the principle of functional isolation argues against the use of language-dependent information in article indexes and favors the use of query-time strategies for applying lexical knowledge. We propose and evaluate a system architecture which embodies this principle. We also show how a storage and retrieval infrastructure based on Burkowski's [BURKOWSKI92] “containment model” abstraction can be employed to implement both the text retrieval and lexicon facilities required in an integrated system.

In this paper, we present an approach to the incorporation of object versioning into a distributed full-text information retrieval system. We propose an implementation based on “partially versioned” index sets, arguing that its space overhead and query-time performance make it suitable for full-text IR, with its heavy dependence on inverted indexing. We develop algorithms for computing both historical queries and time range queries and show how these algorithms can be applied to a number of problems in distributed information management, such as data replication, caching, transactional consistency, and hybrid media repositories.

AI-STARSis a lexicon-assisted full-text Information Retrieval system, designed for use in a dynamic corporate environment.In this paper, we explore how the requirements of such an environment have influenced many key aspects of the design and implementation of the AI-STARS system.We promote the use of "views" to create logical partitions in large, heterogeneous databases, and argue that storing not only article instances, but also class defhitions, stored queries, display templates and linguistic data in a single object repository has consequences that can be exploited for schema and lexicon evolution, security and subject filtering, information navigation, and data distribution.structured information objects.Next we consi&r querying on semi-stmctured objects and show how the notion of storing queries can & exploited to support multiple logical "views" of large databases.We then describe how our lexical and linguistic knowledge can take advantage of the object-oriented infrastmcture as well, arguing that a uniform representation of heterogeneous objects simplifies the tasks of data security, p~tioning, and distribution.We conclude with a dkcussion of implementation status and issues, related research, and user interface implications.

This paper describes the design of a direct manipulation user interface for Boolean information retrieval. Intended to overcome the difficulties of manipulating explicit Boolean queries as well as the “black box” drawbacks of so-called natural language query systems, the interface presents a two-dimensional graphical representation of a user's natural language query which not only exposes heuristic query transformations performed by the system, but also supports query reformulation by the user via direct manipulation of the representation. The paper illustrates the operation of the interface as implemented in the AI-STARS full-text information retrieval system.