Exploring accumulative query expansion for relevance feedback

For the participation of Dublin City University (DCU) in the Relevance Feedback (RF) track of INEX 2010, we investigated the relation between the length of relevant text passages and the number of RF terms. In our experiments, relevant passages are segmented into non-overlapping windows of xed length which are sorted by similarity with the query. In each retrieval iteration, we extend the current query with the most frequent terms extracted from these word windows. The number of feedback terms corresponds to a constant number, a number proportional to the length of relevant passages, and a number inversely proportional to the length of relevant passages, respectively. Retrieval experiments show a signicant increase in MAP for INEX 2008 training data and improved precisions at early recall levels for the 2010 topics as compared to the baseline Rocchio feedback

A Comparison of Underlying Strategies for Improving Reading Comprehension and Retention

Much interest currently exists in topics related to the improvement of learning through such avenues as mathemagenic activities, and a considerable amount of research has been done recently in the area of visual cueing as a learning enhancement technique. This paper is restricted to a discussion of one visual cueing technique underlining and some implications arising from underlining research

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

An adaptive technique for content-based image retrieval

We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search

Looking at Vector Space and Language Models for IR using Density Matrices

In this work, we conduct a joint analysis of both Vector Space and Language Models for IR using the mathematical framework of Quantum Theory. We shed light on how both models allocate the space of density matrices. A density matrix is shown to be a general representational tool capable of leveraging capabilities of both VSM and LM representations thus paving the way for a new generation of retrieval models. We analyze the possible implications suggested by our findings.Comment: In Proceedings of Quantum Interaction 201

Fly's time

The struggle between public and private efforts to sequence the fly genome is the subject of Michael Ashburner's new book, Won for All: How the Drosophila Genome Was Sequence

An information-theoretic framework for semantic-multimedia retrieval

This article is set in the context of searching text and image repositories by keyword. We develop a unified probabilistic framework for text, image, and combined text and image retrieval that is based on the detection of keywords (concepts) using automated image annotation technology. Our framework is deeply rooted in information theory and lends itself to use with other media types. We estimate a statistical model in a multimodal feature space for each possible query keyword. The key element of our framework is to identify feature space transformations that make them comparable in complexity and density. We select the optimal multimodal feature space with a minimum description length criterion from a set of candidate feature spaces that are computed with the average-mutual-information criterion for the text part and hierarchical expectation maximization for the visual part of the data. We evaluate our approach in three retrieval experiments (only text retrieval, only image retrieval, and text combined with image retrieval), verify the framework’s low computational complexity, and compare with existing state-of-the-art ad-hoc models