Natural Language Generation For Sponsored-Search Advertisements
EC '08: ACM Conference on Electronic Commerce Chicago Il USA July, 2008(2008)
摘要
In sponsored search, advertisers bid on phrases representative of offered products or services.. For large advertisers, these phrases often come from quasi-algorithmically generated lists of thousands of terms prone to poor linguistic construction. A bidded term by itself is usually unsuitable for direct insertion into an ad copy template; it must be rephrased and capitalized properly to fit the template, possibly with additional language to avoid semantic ambiguity. We develop a natural language generation system to automate these steps, preparing a list of terms for insertion into an ad template. For each input term, our system first finds a proper word ordering by mining a corpus of Web search query logs. Next it determines whether the term is ambiguous and-if semantics dictate-attaches a clarifying modifier culled from query logs. Finally, it applies proper capitalization by analyzing pages from Web search engine results. Each step yields a plausible set of displayable forms from which a machine-learned model selects the best. The models are trained and tested on a large set of human-labeled data. The overall system significantly outperforms baseline systems that use simple heuristics.
更多查看译文
关键词
Sponsored search,pay-per-click,natural language processing,natural language generation,automation,query log,machine learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要