Evaluating evaluation measure stability
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Learning Algorithms for Keyphrase Extraction
Information Retrieval
Information retrieval system evaluation: effort, sensitivity, and reliability
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Improved automatic keyword extraction given more linguistic knowledge
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Finding advertising keywords on web pages
Proceedings of the 15th international conference on World Wide Web
Keyword extraction for contextual advertisement
Proceedings of the 17th international conference on World Wide Web
Domain-specific keyphrase extraction
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Proceedings of the 20th international conference companion on World wide web
Proceedings of the fifth ACM international conference on Web search and data mining
Hybrid-ε-greedy for mobile context-aware recommender system
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Selecting keywords to represent web pages using Wikipedia information
Proceedings of the 18th Brazilian symposium on Multimedia and the web
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Contextual Advertising (CA) refers to the placement of ads that are contextually related to the web page content. The science of CA deals with the task of finding advertising keywords from web pages. We present a different candidate selection method to extract advertising keywords from a web page. This method makes use of Part-of-Speech (POS) patterns that restrict the number of potential candidates a classifier has to handle. It fetches words/phrases that belong to the selected set of POS patterns. We design four systems based on chunking method and the features they use. These systems are trained on a naive Bayes classifier with a set of web pages annotated with 'advertising' keywords. The systems can then find advertising keywords from previously unseen web pages. Empirical evaluation shows that systems using the proposed chunking method perform better than the systems using N-Gram based chunking. All improvements in the systems are found statistically significant at a 99% confidence interval.