Query enrichment for web-query classification

By Dou Shen, Rong Pan, Jeffrey Junfeng Pan, Kangheng Wu, Jie Yin, Qiang Yang. 2006

174 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search

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. Jianfeng Gao
2. Li Deng
3. Xiaodong He
4. Xiaodong Liu
5. W. Bruce Croft
6. Hang Li
7. Hamed Zamani
8. Jun Xu

In Information Systems

1. Jun Xu
2. Jianfeng Gao
3. Xiaodong He
4. Hang Li
5. Li Deng
6. Hamed Zamani
7. W. Bruce Croft
8. Jamie Callan

In Information Retrieval & Search

1. Jianfeng Gao
2. Xiaodong He
3. Hamed Zamani
4. Li Deng
5. Jun Xu
6. Jamie Callan
7. Hang Li
8. W. Bruce Croft

Citations in the corpus

• Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval (2015, background): “It is however challenging because queries tend to be short (Shen et al., 2006).”
• 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): “More recently, a number of methods were proposed to enrich queries using external information, such as the top-retrieved documents [14, 32] and query context [6].”