NLPR at TREC 2003: Novelty and Robust

NLPR at TREC 2004: Robust Experiments

It is the second time that the Chinese Information Processing group of NLPR participates in TREC. In the past, we have investigated the use of two key technologies: Window-based weighting method and Semantic Tree Model for query expansion, with success, to tasks in novelty and robust tracks. We focused on the Robust Retrieval Track at this year’s conference. Based on the previous IR architectur...

NLPR at TREC 2005: HARD Experiments

1 Overview It is the third time that Chinese Information Processing Group of NLPR takes part in TREC. In the past, we participated in Novelty track and Robust track, in which we had evaluated our two key notions: Window-based Retrieval Algorithm and Result Emerging Strategy [1][2]. This year we focus on investigating the significance of relevance feedback, so HARD track is our best choice. HARD...

THUIR at TREC 2003: Novelty, Robust and Web

This is the second time that Tsinghua University Information Retrieval Group (THUIR) participates in TREC. In this year, we took part in four tracks: novelty, robust, web and HARD, describing in following sections, respectively. A new IR system named TMiner has been built on which all experiments have been performed. In the system, Primary Feature Model (PFM) has been proposed and combined with...

Trec Novelty Track At IRIT-SIG

In TREC 2003, IRIT improved the strategy that was introduced in TREC 2002. A sentence is considered as relevant if it matches the topic with a certain level of coverage. This coverage depends on the category of terms used in the texts. Different types of terms have been defined: highly relevant, scarcely relevant, nonrelevant and highly non-relevant. With regard to the novelty part, a sentence ...

Meiji University Web and Novelty Track Experiments at TREC 2003