FUB at TREC 2009 Relevance Feedback Track: Diversifying Feedback Documents (Extended Abstract)

FUB at TREC 2008 Relevance Feedback Track: Extending Rocchio with Distributional Term Analysis

CMIC@TREC 2009: Relevance Feedback Track

This paper describes CMIC’s submissions to the TREC’09 relevance feedback track. In the phase 1 runs we submitted, we experimented with two different techniques to produce 5 documents to be judged by the user in the initial feedback step, namely using knowledge bases and clustering. Both techniques attempt to topically diversify these 5 documents as much as possible in an effort to maximize the...

Topical Diversity and Relevance Feedback

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.

Relevance Feedback Based on Constrained Clustering: FDU at TREC 09

We introduce our participation of the TREC Relevance Feedback(RF) TRACK in 2009. The RF09 TRACK is focused on the explicit relevant feedback, where a few relevant and irrelevant documents are available to each query. Our system is implemented under the framework of probabilistic language model. We apply the constrained clustering on the top returned documents and extract the expanded words to r...

UCSC at Relevance Feedback Track

The relevance feedback track in TREC 2009 focuses on two sub tasks: actively selecting good documents for users to provide relevance feedback and retrieving documents based on user relevance feedback. For the first task, we tried a clustering based method and the Transductive Experimental Design (TED) method proposed by Yu et al. [5]. For clustering based method, we use the K-means algorithm to...