Generalizing from relevance feedback using named entity wildcards

Traditional adaptive filtering systems learn the user’s interests in a rather simple way – words from relevant documents are favored in the query model, while words from irrelevant documents are down-weighted. This biases the query model towards specific words seen in the past, causing the system to favor documents containing relevant but redundant information over documents that use previously unseen words to denote new facts about the same news event. This paper proposes news ways of generalizing from relevance feedback by augmenting the traditional bagof-words query model with named entity wildcards that are anchored in context. The use of wildcards allows generalization beyond specific words, while contextual restrictions limit the wildcard-matching to entities related to the user’s query. We test our new approach in a nuggetlevel adaptive filtering system and evaluate it in terms of both relevance and novelty of the presented information. Our results indicate that highe...

• Type: Conference
• Year: 2007
• Where: CIKM
• Authors: Abhimanyu Lad, Yiming Yang
• Keywords: CIKM 2007, Query Model, Relevance Feedback, Wildcard