One of the most prevalent online advertising methods is textual advertising. To produce a textual ad, an advertiser must craft a short creative (the text of the ad) linking to a landing page, which describes the product or service being promoted. Furthermore, the advertiser must associate the creative to a set of manually chosen bid phrases representing those Web search queries that should trigger the ad. For efficiency, given a landing page, the bid phrases are often chosen first, and then for each bid phrase the creative is produced using a template. Nevertheless, an ad campaign (e.g., for a large retailer) might involve thousands of landing pages and tens or hundreds of thousands of bid phrases, hence the entire process is very laborious.

Our study aims towards the automatic construction of online ad campaigns: given a landing page, we propose several algorithmic methods to generate bid phrases suitable for the given input. Such phrases must be both relevant (that is, reflect the content of the page) and well-formed (that is, likely to be used as queries to a Web search engine). To this end, we use a two phase approach. First, candidate bid phrases are generated by a number of methods, including a (monolingual) translation model capable of generating phrases not contained within the text of the input as well as previously "unseen" phrases. Second, the candidates are ranked in a probabilistic framework using both the translation model, which favors relevant phrases, as well as a bid phrase language model, which favors well-formed phrases.

Empirical evaluation based on a real-life corpus of advertiser-created landing pages and associated bid phrases confirms the value of our approach, which successfully re-generates many of the human-crafted bid phrases and performs significantly better than a pure text extraction method.