A text-based approach to the ImageCLEF 2010 photo annotation task

The challenges of searching the increasingly large collections of digital images which are appearing in many places mean that automated annotation of images is becoming an important task. We describe our participation in the ImageCLEF 2010 Visual Concept Detection and Annotation Task. Our approach used only the textual features (Flickr user tags and EXIF information) to perform the automatic annotation. Our approach was to explore the use of different techniques to improve the results of textual annotation. We identify the drawbacks of our approach and how these might be addressed and optimized in further work

Enhanced information retrieval using domain-specific recommender models

The objective of an information retrieval (IR) system is to retrieve relevant items which meet a user information need. There is currently significant interest in personalized IR which seeks to improve IR effectiveness by incorporating a model of the user’s interests. However, in some situations there may be no opportunity to learn about the interests of a specific user on a certain topic. In our work, we propose an IR approach which combines a recommender algorithm with IR methods to improve retrieval for domains where the system has no opportunity to learn prior information about the user’s knowledge of a domain for which they have not previously entered a query. We use search data from other previous users interested in the same topic to build a recommender model for this topic. When a user enters a query on a topic, new to this user, an appropriate recommender model is selected and used to predict a ranking which the user may find interesting based on the behaviour of previous users with similar queries. The recommender output is integrated with a standard IR method in a weighted linear combination to provide a final result for the user. Experiments using the INEX 2009 data collection with a simulated recommender training set show that our approach can improve on a baseline IR system

A proposal for the evaluation of adaptive information retrieval systems using simulated interaction

The Centre for Next Generation Localisation (CNGL) is involved in building interactive adaptive systems which combine Information Retrieval (IR), Adaptive Hypermedia (AH) and adaptive web techniques and technologies. The complex functionality of these systems coupled with the variety of potential users means that the experiments necessary to evaluate such systems are difficult to plan, implement and execute. This evaluation requires both component-level scientific evaluation and user-based evaluation. Automated replication of experiments and simulation of user interaction would be hugely beneficial in the evaluation of adaptive information retrieval systems (AIRS). This paper proposes a methodology for the evaluation of AIRS which leverages simulated interaction. The hybrid approach detailed combines: (i) user-centred methods for simulating interaction and personalisation; (ii) evaluation metrics that combine Human Computer Interaction (HCI), AH and IR techniques; and (iii) the use of qualitative and quantitative evaluations. The benefits and limitations of evaluations based on user simulations are also discussed

Towards evaluation of personalized and collaborative information retrieval

We propose to extend standard information retrieval (IR) ad-hoc test collection design to facilitate research on personalized and collaborative IR by gathering additional meta-information during the topic (query) development process. We propose a controlled query generation process with activity logging for each topic developer. The standard ad-hoc collection will thus be accompanied by a new set of thematically related topics and the associated log information, and has the potential to simulate a real-world search scenario to encourage retrieval systems to mine user information from the logs to improve IR effectiveness. The proposed methodology described in this paper will be applied in a pilot task which is scheduled to run in the FIRE 2011 evaluation campaign. The task aims at investigating the research question of whether personalized and collaborative IR retrieval experiments and evaluation can be pursued by enriching a standard ad-hoc collection with such meta-information

Overview of the personalized and collaborative information retrieval (PIR) track at FIRE-2011

The Personalized and collaborative Information Retrieval (PIR) track at FIRE 2011 was organized with an aim to extend standard information retrieval (IR) ad-hoc test collection design to facilitate research on personalized and collaborative IR by collecting additional meta-information during the topic (query) development process. A controlled query generation process through task-based activities with activity logging was used for each topic developer to construct the final list of topics. The standard ad-hoc collection is thus accompanied by a new set of thematically related topics and the associated log information. We believe this can better simulate a real-world search scenario and encourage mining user information from the logs to improve IR effectiveness. A set of 25 TREC formatted topics and the associated metadata of activity logs were released for the participants to use. In this paper we illustrate the data construction phase in detail and also outline two simple ways of using the additional information from the logs to improve retrieval effectiveness

Enhanced information retrieval by exploiting recommender techniques in cluster-based link analysis

Inspired by the use of PageRank algorithms in document ranking, we develop and evaluate a cluster-based PageRank algorithm to re-rank information retrieval (IR) output with the objective of improving ad hoc search effectiveness. Unlike existing work, our methods exploit recommender techniques to extract the correlation between documents and apply detected correlations in a cluster-based PageRank algorithm to compute the importance of each document in a dataset. In this study two popular recommender techniques are examined in four proposed PageRank models to investigate the effectiveness of our approach. Comparison of our methods with strong baselines demonstrates the solid performance of our approach. Experimental results are reported on an extended version of the FIRE 2011 personal information retrieval (PIR) data collection which includes topically related queries with click-through data and relevance assessment data collected from the query creators. The search logs of the query creators are categorized based on their different topical interests. The experimental results show the significant improvement of our approach compared to results using standard IR and cluster-based PageRank methods

Comparative evaluation of query expansion methods for enhanced search on microblog data: DCU ADAPT @ SMERP 2017 workshop data challenge

The rapid growth in the availability of social media content posted during emergency situations is creating significant interest in research into how this information can be exploited to assist emergency relief operations and to help with emergency preparedness and in early warning systems. We describe the DCU ADAPT Centre participation in the microblog search data challenge at the SMERP 2017 workshop. This task aimed to promote development of information retrieval (IR) methods for practical challenges that need to be addressed during an emergency event, along with comparative evaluation of the methodologies developed for this task. The task is based on a large dataset of microblogs posted during the earthquake in Italy in August 2016, together with a set of query topics provided by the task organisers. For our participation in this task we explored use of three different IR techniques: standard IR query expansion based on an external resource, query expansion based on WordNet and use of query expansio

Cohomological Constructions of Regular Cyclic Coverings of the Platonic Maps

AbstractIn we described the regular maps and hypermaps which are cyclic coverings of the Platonic maps, branched over the face centers, vertices or midpoints of edges. Here we determine cochains by which these coverings can be explicitly constructed

Using WordNet for query expansion: ADAPT @ FIRE 2016 microblog track

User-generated content on social websites such as Twitter is known to be an important source of real-time information on significant events as they occur, for example natural disasters. Our participation in the FIRE 2016 Microblog track, seeks to exploit WordNet as an external resource for synonym-based query expansion to support improved matching between search topics and the target Tweet collection. The results of our participation in this task show that this is an effective method for use with a standard BM25 based information retrieval system for this task