Learning to rank for quantity consensus queries

Web search is increasingly exploiting named entities like persons, places, businesses, addresses and dates. Entity ranking is also of current interest at INEX and TREC. Numerical quantities are an important class of entities, especially in queries about prices and features related to products, services and travel. We introduce Quantity Consensus Queries (QCQs), where each answer is a tight quantity interval distilled from evidence of relevance in thousands of snippets. Entity search and factoid question answering have benefited from aggregating evidence from multiple promising snippets, but these do not readily apply to quantities. Here we propose two new algorithms that learn to aggregate information from multiple snippets. We show that typical signals used in entity ranking, like rarity of query words and their lexical proximity to candidate quantities, are very noisy. Our algorithms learn to score and rank quantity intervals directly, combining snippet quantity and snippet text in...

• Type: Conference
• Year: 2009
• Where: SIGIR
• Authors: Somnath Banerjee, Soumen Chakrabarti, Ganesh Ramakrishnan
• Keywords: Entity Ranking, Information Retrieval, Multiple Promising Snippets, Quantity Intervals, SIGIR 2009