Re-examining the potential effectiveness of interactive query expansion

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

Towards More Effective Techniques for Automatic Query Expansion

Techniques for automatic query expansion from top retrieved documents have recently shown promise for improving retrieval effectiveness on large collections but there is still a lack of systematic evaluation and comparative studies. In this paper we focus on term-scoring methods based on the differences between the distribution of terms in (pseudo-)relevant documents and the distribution of terms in all documents, seen as a complement or an alternative to more conventional techniques. We show that when such distributional methods are used to select expansion terms within Rocchio's classical reweighting scheme, the overall performance is not likely to improve. However, we also show that when the same distributional methods are used to both select and weight expansion terms the retrieval effectiveness may considerably improve. We then argue, based on their variation in performance on individual queries, that the set of ranked terms suggested by individual distributional methods can be combined to further improve mean performance, by analogy with ensembling classifiers, and present experimental evidence supporting this view. Taken together, our experiments show that with automatic query expansion it is possible to achieve performance gains as high as 21.34% over non-expanded query (for non-interpolated average precision). We also discuss the effect that the main parameters involved in automatic query expansion, such as query difficulty, number of selected documents, and number of selected terms, have on retrieval effectiveness

Automatic query expansion: A structural linguistic perspective

A user’s query is considered to be an imprecise description of their information need. Automatic query expansion is the process of reformulating the original query with the goal of improving retrieval effectiveness. Many successful query expansion techniques ignore information about the dependencies that exist between words in natural language. However, more recent approaches have demonstrated that by explicitly modeling associations between terms significant improvements in retrieval effectiveness can be achieved over those that ignore these dependencies. State-of-the-art dependency-based approaches have been shown to primarily model syntagmatic associations. Syntagmatic associations infer a likelihood that two terms co-occur more often than by chance. However, structural linguistics relies on both syntagmatic and paradigmatic associations to deduce the meaning of a word. Given the success of dependency-based approaches and the reliance on word meanings in the query formulation process, we argue that modeling both syntagmatic and paradigmatic information in the query expansion process will improve retrieval effectiveness. This article develops and evaluates a new query expansion technique that is based on a formal, corpus-based model of word meaning that models syntagmatic and paradigmatic associations. We demonstrate that when sufficient statistical information exists, as in the case of longer queries, including paradigmatic information alone provides significant improvements in retrieval effectiveness across a wide variety of data sets. More generally, when our new query expansion approach is applied to large-scale web retrieval it demonstrates significant improvements in retrieval effectiveness over a strong baseline system, based on a commercial search engine

A study on the use of summaries and summary-based query expansion for a question-answering task

In this paper we report an initial study on the effectiveness of query-biased summaries for a question answering 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

A survey on the use of relevance feedback for information access systems

Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems

Using Windmill Expansion for Document Retrieval

SEMIOTIKS aims to utilise online information to support the crucial decision–making of those military and civilian agencies involved in the humanitarian removal of landmines in areas of conflict throughout the world. An analysis of the type of information required for such a task has given rise to four main areas of research: information retrieval, document annotation, summarisation and visualisation. The first stage of the research has focused on information retrieval, and a new algorithm, “Windmill Expansion” (WE) has been proposed to do this. The algorithm uses retrieval feedback techniques for automated query expansion in order to improve the effectiveness of information retrieval. WE is based on the extraction of human–generated written phases for automated query expansion. Top and Second Level expansion terms have been generated and their usefulness evaluated. The evaluation has concentrated on measuring the degree of overlap between the retrieved URLs. The less the overlap, the more useful the information provided. The Top Level expansion terms were found to provide 90% of useful URLs, and the Second Level 83% of useful URLs. Although there was a decline of useful URLs from the Top Level to the Second Level, the quantity of relevant information retrieved has increased. The originality of SEMIOTIKS lies in its use of the WE algorithm to help non–domain specific experts automatically explore domain words for relevant and precise information retrieval