Much attention has been paid to the relative effectiveness of interactive query expansion versus automatic query expansion. Although interactive query expansion has the potential to be an effective means of improving a search, in this paper we show that, on average, human searchers are less likely than systems to make good expansion decisions. To enable good expansion decisions, searchers must have adequate instructions on how to use interactive query expansion functionalities. We show that simple instructions on using interactive query expansion do not necessarily help searchers make good expansion decisions and discuss difficulties found in making query expansion decisions. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]:- search process, relevance feedback.

In this paper we present an evaluation of techniques that are designed to encourage web searchers to interact more with the results of a web search. Two specific techniques are examined: the presentation of sentences that highly match the searcher's query and the use of implicit evidence. Implicit evidence is evidence captured from the searcher's interaction with the retrieval results and is used to automatically update the display. Our evaluation concentrates on the effectiveness and subject perception of these techniques. The results show, with statistical significance, that the techniques are effective and efficient for information seeking.

In this article we describe an evaluation of relevance feedback (RF) algorithms using searcher simulations. Since these algorithms select additional terms for query modification based on inferences made from searcher interaction, not on relevance information searchers explicitly provide (as in traditional RF), we refer to them as implicit feedback models. Weintroduce six different models that base their decisions on the interactions of searchers and use different approaches to rank query modification terms. The aim of this article is to determine which of these models should be used to assist searchers in the systems we develop. To evaluate these models we used searcher simulations that afforded us more control over the experimental conditions than experiments with human subjects and allowed complex interaction to be modeled without the need for costly human experimentation. The simulation-based evaluation methodology measures how well the models learn the distribution of terms across relevant documents (i.e., learn what information is relevant) and how well they improve search effectiveness (i.e., create effective search queries). Our findings show that an implicit feedback model based on Jeffrey’s rule of conditioning outperformed other

[dianek | vijayd | fu] @ email.unc.edu In this paper we investigate the effectiveness of a documentindependent technique for eliciting feedback from users about their information problems. We propose that such a technique can be used to elicit terms from users for use in query expansion and as a follow-up when ambiguous queries are initially posed by users. We design a feedback form to obtain additional information from users, administer the form to users after initial querying, and create a series of experimental runs based on the information that we obtained from the form. Results demonstrate that the form was successful at eliciting more information from users and that this additional information significantly improved retrieval performance. Our results further demonstrate a strong relationship between query length and performance.

ueries using all search facets identified from requests, low complexity was achieved by formulating queries with major facets only. Query expansion was based on a thesaurus, from which the expansion keys were elicited for queries. There were five expansion types: (1) the first query version was an unexpanded, original query with one search key for each search concept (original search concepts) elicited from the test thesaurus; (2) the synonyms of the original search keys were added to the original query; (3) search keys representing the narrower concepts of the original search concepts were added to the original query; (4) search keys representing the associative concepts of the original search concepts were added to the original query; (5) all previous expansion keys were cumulatively added to the original query. Query structure refers to the syntactic structure of a query expression, marked with query operators and parentheses. The structure of queries was either weak (queries with n

Different presentations of candidate expansion terms have not been fully explored in interactive query expansion (IQE). Most existing systems that offer an IQE facility use a list form of presentation. This paper examines an hierarchical presentation of the expansion terms which are automatically generated from a set of retrieved documents, organised in a general to specific manner, and visualised by cascade menus. To evaluate the effectiveness of the presentation, a user test was carried out to compare the hierarchical form with the conventional list form. This shows that users of the hierarchy can complete the expansion task in less time and with fewer terms over those using the lists. Relations between initial query terms and selected expansion terms were also investigated.

Abstract. In classic Information Retrieval systems a relevant document will not be retrieved in response to a query if the document and query representations do not share at least one term. This problem, known as “term mismatch”, has been recognised for a long time by the Information Retrieval community and a number of possible solutions have been proposed. Here I present a preliminary investigation into a new class of retrieval models that attempt to solve the term mismatch problem by exploiting complete or partial knowledge of term similarity in the term space. The use of term similarity enables to enhance classic retrieval models by taking into account non-matching terms. The theoretical advantages and drawbacks of these models are presented and compared with other models tackling the same problem. A preliminary experimental investigation into the performance gain achieved by exploiting term similarity with the proposed models is presented and discussed.

In this paper we report an initial study on the effectiveness of query-biased summaries for a questionanswering task. Our summarisation system presents searchers with short summaries of documents. The summaries are composed of a set of sentences that highlight the main points of the document as they relate to the query. These summaries are also used as evidence for a query expansion algorithm to test the use of summaries as evidence for interactive and automatic query expansion. We present the results of a set of experiments to test these two approaches and discuss the relative success of these techniques. 1

Abstract. Query expansion is a well-known method for improving average effectiveness in information retrieval. However, the most effective query expansion methods rely on costly retrieval and processing of feedback documents. We explore alternative methods for reducing queryevaluation costs, and propose a new method based on keeping a brief summary of each document in memory. This method allows query expansion to proceed three times faster than previously, while approximating the effectiveness of standard expansion. 1

Vocabulacy incompatibilities arise when the terms used to index a document collection are largely unknown, or at least not well-known to the users who eventually search the collection. No matter how comprehensive or well-structured the indexing vocabulacy, it is of little use if it is not used effectively in query formulation. This paper demonstrates that techniques for mapping user queries into the controlled indexing vocabulacy have the potential to radically improve document retrieval performance. We also show how the use of controlled indexing vocabulacy can be employed to achieve performance gains for collection selection. Finally, we demonstrate the potential benefit of combining these two techniques in an interactive retrieval environment. Given a user query, our evaluation approach simulates the human user's choice of terms for query augmentation given a list of controlled vocabulacy terms suggested by a system. This strategy lets us evaluate interactive strategies without the need for human subjects.