Abstract
Keyword generation and expansion are important problems in computational advertising. Keyword suggestion methods help advertisers to find more appropriate keywords. They involve discovering new words or phrases related to the existing keywords. Producing the proper hidden yet semantically relevant keywords is a hard problem. The problems real difficulty is in finding many such words. In this paper we propose an artificial keyword suggester for Albanian language by mimicking the human like systems. The possibility of a human to provide these keywords counts on the richness and deepness of its language and cultural qualifications. In order to provide additional keywords a human must accomplish multiple memory search tasks for meanings of huge number of concepts and their frame of references. Hence the memory of the proposed artificial keyword suggester is based on a large information repository formed by utilizing machine reading techniques for fact extraction from the web. As a memory we indirectly use the Albanian world-wide-web and the Gjirafa.com as a search engine. Complementary, the brain of the system is designed as a spreading activating network. The brain treats provided keywords and finds associations between them and concepts within its memory in order to incrementally compute and propose a new list of potential keywords. Experimental results show that our proposed method can successfully provide suggestion that meets the accuracy and coverage requirements.
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References
Yuan, S., Abidin, A.Z., Sloan, M., Wang, J.: Internet advertising: an interplay among advertisers, Online Publishers, Ad Exchanges and Web Users (2012). arXiv preprint arXiv:1206.1754
Szymanski, B.K., Lee, J.S.: Impact of roi on bidding and revenue in sponsored search advertisement auctions. In: Second Workshop on Sponsored Search Auctions (2006)
IAB internet advertising revenue report: Technical report, Price Waterhouse Coopers, April (2013)
Erik, B., Hu, Y.J., Smith, M.D.: From niches to riches: anatomy of the long tail. Sloan Manag. Rev. 47(4), 67–71 (2006)
Yang, S., Ghose, A.: Analyzing the relationship between organic and sponsored search advertising: positive, negative, or zero interdependence? Mark. Sci. 29, 602–623 (2010)
Ribeiro-Neto, B., Cristo, M., Golgher, P.B., Silva de Moura, E.: Impedance coupling in content-targeted advertising. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 496–503. ACM (2005)
Sanderson, M., Croft, B.: Deriving concept hierarchies from text. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (1999)
Joshi, A., Motwani, R.: Keyword generation for search engine advertising. In: Sixth IEEE International Conference on Data Mining—Workshops (ICDMW 06), pp. 490–496 (2006)
Abhishek, V., Hosanagar, K.: Keyword generation for search engine advertising using semantic similarity between terms. In: Proceedings of the Ninth International Conference on Electronic Commerce, p. 94. ACM (2007)
Billerbeck, B.: Efficient query expansion, Ph.D. thesis, RMIT University, Melbourne, Australia (2005)
Qiu, Y., Frei, H.P.: Concept based query expansion. In: Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (1993)
Carrasco, J., Fain, D., Lang, K., Zhukov, L.: Clustering of bipartite advertiser-keyword graph. In: International Conference on Data Mining (2003)
Canhasi, E.: GSolver: Artificial solver of word association game. ICT Innovations 2015, pp. 49–57. Springer International Publishing, Switzerland (2016)
Yutaka, M., Ishizuka, M.: Keyword extraction from a single document using word co-occurrence statistical information. Int. J. Artif. Intell. Tools 13(01), 157–169 (2004)
Etzioni, O., Banko, M., Cafarella, M.J.: Machine Reading. In: AAAI, pp. 1517–1519. ACM, New York (2006)
Gjirafa Inc.: Search Engine for Albanian Web. http://www.gjirafa.com
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)
Collins, A.M., Loftus, E.F.: A spreading-activation theory of semantic processing. Psychol. Rev. 82(6), 407 (1975)
Anderson, J.R.: A spreading activation theory of memory. J. Verbal Learn. Verbal Behav. 22(3), 261–295 (1983)
Canhasi E., Kononenko, I.: Multi-document summarization via archetypal analysis of the content-graph joint model. Knowl. Inf. Syst. 41(3), 821–842. Springer (2014)
Canhasi, E., Kononenko, I.: Automatic extractive multi-document summarization based on archetypal analysis. Non-negative Matrix Factorization Techniques, pp. 75–88. Springer, Berlin (2016)
Canhasi, E., Kononenko, I.: Weighted hierarchical archetypal analysis for multi-document summarization. Comput. Speech Lang. 37, 24–46 (2016)
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Canhasi, E. (2016). Albanian Advertising Keyword Generation and Expansion via Hidden Semantic Relations. In: Silhavy, R., Senkerik, R., Oplatkova, Z., Silhavy, P., Prokopova, Z. (eds) Artificial Intelligence Perspectives in Intelligent Systems. Advances in Intelligent Systems and Computing, vol 464. Springer, Cham. https://doi.org/10.1007/978-3-319-33625-1_34
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DOI: https://doi.org/10.1007/978-3-319-33625-1_34
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