Web search engines sift through billions of documents to identify those most likely to be relevant to a short natural language query. This functionality can be exploited to relate queries not just to documents but also to other concepts and queries. In this talk, we describe several applications of this principle, including the generation of query refinement suggestions for interactive search assistance and the discovery of alternative descriptors for an advertiser’s product space.
Peter Anick is a member of the Applied Sciences group at Yahoo! where he currently works on developing infrastructure and tools for supporting online query assistance, such as Yahoo’s recently released “Search Assist” product. He received his PhD in computer science from Brandeis University in 1999. Prior to that, he worked for many years in Digital Equipment Corporation’s Artificial Intelligence Technology groups on applications of computational linguistics, including online text search for customer support and natural language interfaces for expert and database systems, and subsequently at AltaVista and Overture. His research interests include intelligent information retrieval, user interfaces for exploratory search, text data mining and lexical semantics of nouns and noun compounds. He is a member of ACM SIGIR, former editor of SIGIR Forum and current workshops program chair for SIGIR’08.