Building bridges for web query classification

By Dou Shen, Qiang Yang, Zheng Chen. 2006

319 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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. Jiafeng Guo
2. Xueqi Cheng
3. Wayne Xin Zhao
4. Xiao Li
5. Li Deng
6. Hang Li
7. Jianfeng Gao
8. Dong Yu

In Information Systems

1. Xueqi Cheng
2. Jiafeng Guo
3. Wayne Xin Zhao
4. Gao Cong
5. A. Acero
6. Xiao Li
7. Ye-Yi Wang
8. Tongshuang Sherry Wu

Citations in the corpus

• Understanding the Semantic Structure of Noun Phrase Queries (2010, background): “As mentioned in the introduction, previous works on query intent understanding have largely focused on classification, i.e. , automatically mapping queries into semantic classes (Shen et al., 2006; Li et al., 2008b; Arguello et al., 2009).”
• Learning query intent from regularized click graphs (2008, methodology): “[16] used search engine results as features, including pages, snippets and titles, and built classifiers based on a document taxonomy; then classifications in the document taxonomy were mapped to those in the target taxonomy.”
• Tempura: Query Analysis with Structural Templates (2020, background): “Meanwhile, without isolating features, researchers have also tried to organize the dataset by automatically classifying [36, 3] or clustering [1] similar queries together.”
• Mining User Intents in Twitter: A Semi-Supervised Approach to Inferring Intent Categories for Tweets (2015, background): “2009; Kang and Kim 2003); and the other type is classifying queries according to the user intent, such as “jobs” or “travel” (Hu et al. 2009; Shen et al. 2006; Beitzel et al. 2007; Li, Wang, and Acero 2008).”
• Named entity recognition in query (2009, background): “Query classification falls into two groups: (1) classification according to search intent, such as informational, navigational or transactional [7, 21, 16]; (2) classification according to semantics of query, such as “Shopping” or “Living” [24, 1].”