This paper describes the system developed by the Language and Reasoning Group of UAM for the Relevance Feedback track of INEX 2012. The presented system focuses on the problem of ranking documents in accordance to their relevance. It is mainly based on the following hypotheses: (i) current IR machines are able to retrieve relevant documents for most of general queries, but they can not generate...

A method is proposed that makes use of visual reranking to selectively sample feedback sets for Pseudo-Relevance-Feedback during speechtranscript-based video retrieval. Observed performance improvement is indicative of the ability of visual reranking to increase the relevance density of the feedback set.

This paper describes our corpus annotation project. The annotated corpus has relevance tags which consist of predicate-argument relations, relations between nouns, and coreferences. To construct this relevance-tagged corpus, we investigated a large corpus and established the specification of the annotation. This paper shows the specification and difficult tagging problems which have emerged thr...

This year the relevance feedback track further examined relevance feedback with a single document relevance feedback task. Seven groups participated in the track. At this time, relevance judging is on-going and no results are available. This notebook version of the track describes the track, presents the current status of the track, and includes participant summaries.

We describe the participation of the University of Amsterdam’s Intelligent Systems Lab in the relevance feedback track at TREC 2009. Our main conclusion for the relevance feedback track is that a topical diversity approach provides good feedback documents. Further, we find that our relevance feedback algorithm seems to help most when there are sufficient relevant documents available.

The focus of our participation was optimal selection and use of diverse feedback documents. Assuming that the query has a topical structure and that the user is interested only in some query topics and assuming also that only a small amount of feedback information will be made available, the goal was to select topic representatives to be used as feedback documents and then exploit the feedback ...

Highly applicable research is done not only by some IS faculty members, but also by software firms, consulting firms, and other organizations whose products and services depend on IS research they perform. The applicability of IS research done by academics is evident in the concepts and explanations in many textbooks. There should be little surprise, however, that practitioners who expect reada...

The Bayesian relevance-feedback approach introduced with the PicHunter system 5] is extended to include hidden semantic attributes. The general approach is motivated and experimental results are presented that demonstrate signiicant reductions in search times (28-32%) using these annotations.

In this paper we report on the application of two contrasting types of relevance feedback for web retrieval. We compare two systems; one using explicit relevance feedback (where searchers explicitly have to mark documents relevant) and one using implicit relevance feedback (where the system endeavours to estimate relevance by mining the searcher's interaction). The feedback is used to update th...

Relevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the Pseudo Relevance Feedback task. It is known that one of the factors that more affect to the Pseudo Relevance Fee...