Finding advertising keywords on web pages

By Wen-tau Yih, Joshua Goodman, Vitor R. Carvalho. The Web Conference, 2006

377 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/8054a2ea6ad690ba50298bee60ef2715d88eb8ab

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Citations in the corpus

Multi-label learning with millions of labels: recommending advertiser bid phrases for web pages (2013, methodology): “ing bid phrase suggestions using precision and the edit distance at the word level [32, 46].”
Modeling Interestingness with Deep Neural Networks (2014, neutral): “In addition to the notion of relevance as described in Section 1, related to interestingness is also the notion of salience (also called aboutness ) (Gamon et al. 2013; 2014; Parajpe 2009; Yih et al. 2006).”
Automatic Keyphrase Extraction: A Survey of the State of the Art (2014, methodology): “…a candidate keyphrase has been encoded as (1) a PoS tag sequence, which denotes the sequence of part-of-speech tag(s) assigned to its word(s); and (2) a suffix sequence, which is the sequence of morphological suffixes of its words (Yih et al., 2006; Nguyen and Kan, 2007; Kim and Kan, 2009).”
Discovering key concepts in verbose queries (2008, background): “Automatic extraction of concepts of interest from a larger body of text have proved to be useful for summarization [16], keyword extraction [15], content-targeted advertising [33], named entity recognition [4] and document clustering [11].”
Just-in-time contextual advertising (2007, background): “[28], all prior works mostly experimented with the different parts of the ad, assuming the publisher’s page is given in its entirety.”
Real-time bidding for online advertising: measurement and analysis (2013, neutral): “A more advanced approach is to learn a model including various features of webpages, which could then be used to compute a relevance score of advertisers’ targeting criteria [17, 7, 8].”
Single Document Keyphrase Extraction Using Neighborhood Knowledge (2008, background): “Other related works include web page keyword extraction (Kelleher and Luz, 2005), advertising keywords finding (Yih et al., 2006).”