Query type classification for web document retrieval

By Inho Kang, Gil-Chang Kim. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2003

305 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/0f66ae3575cd750ec24fd89d5b5f6c17fcf40e22

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hamed Zamani
2. Jun Xu
3. W. Bruce Croft
4. Hang Li
5. Wayne Xin Zhao
6. Jimmy J. Lin
7. Qiang Yang
8. Jamie Callan

In Information Systems

1. Jun Xu
2. Hamed Zamani
3. Hang Li
4. Bernard Jim Jansen
5. W. Bruce Croft
6. Danielle L. Booth
7. Qiang Yang
8. Amanda Spink

Citations in the corpus

• Query enrichment for web-query classification (2006, background): “To classify these queries into certain target categories is a dif.cult but important problem.”
• Building bridges for web query classification (2006, background): “proposed to classify queries according to their functional types [4].”
• Learning to rank for information retrieval (2009, unknown): “• Query-dependent ranking [52, 71], which adopts different ranking models for different types of queries, based on either hard query type classification or the soft nearest neighbor based approach.”
• Query Expansion Techniques for Information Retrieval: a Survey (2017, unknown): “Most web queries fall under the three fundamental categories [49,137] :”
• Q2C@UST: our winning solution to query classification in KDDCUP 2005 (2005, background): “…Jie Yin and Qiang Yang Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China {dshen, panrong, jtsun, panjf, khwu,yinjie,qyang}@cs.ust.hk http://webproject1.cs.ust.hk/q2c/ ABSTRACT In this paper, we describe our…”
• Mining User Intents in Twitter: A Semi-Supervised Approach to Inferring Intent Categories for Tweets (2015, background): “The first 1Statistics from Twitter show that approximately 500 million tweets are sent every day: http://goo.gl/407UDX. type is classifying queries according to the query type, such as informational or navigational or transactional (Cao et al. 2009; Kang and Kim 2003); and the other type is…”
• Semantic Matching in Search (2014, background): “Query classification [97, 113, 13, 161, 119] and document classification should be helpful for addressing query document mismatch.”
• Estimating Embedding Vectors for Queries (2016, methodology): “The usefulness of the query classification task for web search has been shown in the literature, e.g., [19].”
• A review of ontology based query expansion (2007, background): “Kang and Kim (2003) also produce similar query classifications.”
• Determining the informational, navigational, and transactional intent of Web queries (2008, methodology): “Lee et al. (2005) used 50 queries, and Kang and Kim (2003) used 200 queries.”