In TREC 2011, we focus on tackling the new challenges proposed by the pilot Crowdsourcing and Microblog tracks, using our Terrier Information Retrieval Platform. Meanwhile, we continue to build upon our novel xQuAD framework and data-driven ranking approaches within Terrier to achieve effective and efficient ranking for the TREC Web track. In particular, the aim of our Microblog track participa...

We propose a personalized re-ranking algorithm through mining user dwell times derived from a user’s previously online reading or browsing activities. We acquire document level user dwell times via a customized web browser, from which we then infer concept word level user dwell times in order to understand a user’s personal interest. According to the estimated concept word level user dwell time...

Invenio is the web-based integrated digital library system developed at CERN. Within this framework, we present four types of ranking models based on the citation graph that complement the simple approach based on citation counts: time-dependent citation counts, a relevancy ranking which extends the PageRank model, a time-dependent ranking which combines the freshness of citations with PageRank...

In this year’s QA Track, we participant in the main and document ranking task and do not take part in the relation task. We put the most effort in factoid and definition questions, and very little on list questions and document ranking task. For factoid questions, we use three QA systems: system 1, system 2 and system 3. System 1 is very similar to our last year’s system [Wu et al, 2004] except...

Fast and effective ranking of a collection of documents with respect to a query requires several structures, including a vocabulary, inverted file entries, arrays of term weights and document lengths, an array of partial similarity accumulators, and address tables for inverted file entries and documents. Of all of these structures, the array of document lengths and the array of accumulators are...

Now-a-days the web based information is increasing day-by-day. As the number of internet users and the number of web document grows, it is difficult for users to find the documents that are relevant to their particular needs.In Recent years semantic web search is not considering semantic relations between words in traditional Machine Learning algorithms. Previous works on ontology-based semanti...

Traditional ranking models for information retrieval lack the ability to make a clear distinction between relevant and nonrelevant documents at top ranks if both have similar bag-of-words representations with regard to a user query. We aim to go beyond the bag-of-words approach to document ranking in a new perspective, by representing each document as a sequence of sentences. We begin with an a...

This paper describes the system WHUSUM we developed to participate in the update summarization task of TAC 2009. Given a topic and corresponding topic statement, this year's task is to write 2 summaries (one for Document Set A and one for Document Set B) that meet the information need expressed in the topic statement. In order to generate a topic-oriented summary for Set A, We present a co-trai...

Probabilistic retrieval models usually rank documents based on a scalar quantity. However, such models lack any estimate for the uncertainty associated with a document’s rank. Further, such models seldom have an explicit utility (or cost) that is optimized when ranking documents. To address these issues, we take a Bayesian perspective that explicitly considers the uncertainty associated with th...

Recently, mean-variance analysis has been proposed as a novel paradigm to model document ranking in Information Retrieval. The main merit of this approach is that it diversifies the ranking of retrieved documents. In its original formulation, the strategy considers both the mean of relevance estimates of retrieved documents and their variance. However, when this strategy has been empirically in...