KDD CUP-2005 report: facing a great challenge

By Y. Li, Zijian Zheng, Honghua (Kathy) Dai. 2005

123 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems

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Citations in the corpus

Relevance-based Word Embedding (2017, methodology): “We consider the dataset that was introduced in KDD Cup 2005 [22] for the internet user search query categorization task and was previously used in [41] for evaluating query embedding vectors.”
Building bridges for web query classification (2006, background): “As summarized by the organizers [6], most solutions expanded the queries through search engines or WordNet and expanded the category by mapping between some pre-deﬁned/existing taxonomy to the target taxonomy.”
Estimating Embedding Vectors for Queries (2016, methodology): “Given the importance of the query classification task in web search, the 2005 ACM Knowledge Discovery and Data Mining competition (called, KDD Cup 2005) [26] focused on this task: classifying the queries issued by real web users based on query topics.”
Piggyback: Using Search Engines for Robust Cross-Domain Named Entity Recognition (2011, background): “The 2005 KDD Cup is a query topic categorization task based on 800,000 queries (Li et al., 2005).”
A large-scale evaluation and analysis of personalized search strategies (2007, methodology): “User s pro.le cl (u) is presented as a weighting vector of 67 pre-de.ned topic categories provided by KDD Cup-2005 [18].”
Query enrichment for web-query classification (2006, methodology): “For the testing dataset, since the three labelers are asked to label the queries, the results reported are the average values [Li et al. 2005].”
Opinion Mining and Sentiment Analysis (2008, methodology): “The queries were drawn from the publicly available list of real MSN users’ queries released for the 2005 KDD Cup competition [185]; the KDD data itself is available at http://www.”
Mining Query Logs: Turning Search Usage Data into Knowledge (2010, methodology): “The score SL−Profile(q, p, u) is, in this case, computed by using a user profile specified as a vector cl(u) of 67 pre-defined topic categories defined by the 2005 KDD Cup [137].”