Hierarchic document clustering has been widely applied to Information Retrieval (IR) on the grounds of its potential improved effectiveness over inverted file search. However, previous research has been inconclusive as to whether clustering does bring improvements. In this paper we take the view that if hierarchic clustering is applied to search results (query-specific clustering), then it has the potential to increase the retrieval effectiveness compared both to that of static clustering and of conventional inverted file search. We conducted a number of experiments using five document collections and four hierarchic clustering methods. Our results show that the effectiveness of query-specific clustering is indeed higher, and suggest that there is scope for its application to IR.

In this paper we propose the clustering of top-ranking sentences (TRS) for e#ective information access. Top-ranking sentences are selected by a query-biased sentence extraction model. By clustering such sentences, we aim to generate and present to users a personalised information space. We outline our approach in detail and we describe how we plan to utilise user interaction with this space for e#ective information access. We present an initial evaluation of TRS clustering by comparing its e#ectiveness at providing access to useful information to that of document clustering.

Abstract. This paper describes Armil, a meta-search engine that groups into disjoint labelled clusters the Web snippets returned by auxiliary search engines. The cluster labels generated by Armil provide the user with a compact guide to assessing the relevance of each cluster to her information need. Striking the right balance between running time and cluster well-formedness was a key point in the design of our system. Both the clustering and the labelling tasks are performed on the fly by processing only the snippets provided by the auxiliary search engines, and use no external sources of knowledge. Clustering is performed by means of a fast version of the furthest-point-first algorithm for metric kcenter clustering. Cluster labelling is achieved by combining intra-cluster and inter-cluster term extraction based on a variant of the information gain measure. We have tested the clustering effectiveness of Armil against Vivisimo, the de facto industrial standard in Web snippet clustering, using as benchmark a comprehensive set of snippets obtained from the Open Directory Project hierarchy. According to two widely accepted “external” metrics of clustering quality, Armil achieves better performance levels by 10%. We also report the results of a thorough user evaluation of both the clustering and the cluster labelling algorithms. 1

By considering sentences selected by a query-biased sentence extraction model from the top-retrieved documents, we create a personalised information space which is characterised by the presence of search terms. We cluster this information space, and enable searchers to interact with the resulting clusters. In order to examine whether users can recognise, and benefit from, the clustered organisation, we compare user interaction and performance between an actual clustering and a pseudo-clustering of the information space for completing information seeking tasks. The results provide evidence for the utility and meaningfulness of the clustered organisation.

Abstract: By considering sentences selected by a query-biased sentence extraction model from the top-retrieved documents, we create a personalised information space which is characterised by the presence of search terms. We cluster this information space, and enable searchers to interact with the resulting clusters. In order to examine whether users can recognise, and benefit from, the clustered organisation, we compare user interaction and performance between an actual clustering and a pseudo-clustering of the information space for completing information seeking tasks. The results provide evidence for the utility and meaningfulness of the clustered organisation.

Abstract. This paper presents a user study that evaluated the effectiveness of an aggregated search interface in the context of non-navigational search tasks. An experimental system was developed to present search results aggregated from multiple information sources, and compared to a conventional tabbed interface. Sixteen participants were recruited to evaluate the performance of the two interfaces. Our results suggest that the aggregated search interface is a promising way of supporting nonnavigational search tasks. The quantity and diversity of the retrieved items which participants accessed to complete a task, increased in the aggregated interface. Participants also found the aggregated presentation easier to access to retrieved items and to find relevant information, compared to the conventional interface. 1