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Generating Appealing Brand Names

  • Gaurush HiranandaniEmail author
  • Pranav Maneriker
  • Harsh Jhamtani
Conference paper
  • 442 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10762)

Abstract

Providing appealing brand names to newly launched products, newly formed companies or for renaming existing companies is highly important as it can play a crucial role in deciding its success or failure. In this work, we propose a computational method to generate appealing brand names based on the description of such entities. We use quantitative scores for readability, pronounceability, memorability and uniqueness of the generated names to rank order them. A set of diverse appealing names is recommended to the user for the brand naming task. Experimental results show that the names generated by our approach are more appealing than names which prior approaches and recruited humans could come up.

Notes

Acknowledgments

We thank Dr. Niloy Ganguly for providing valuable comments and feedback.

References

  1. 1.
    Ries, A., Trout, J.: Positioning: The Battle for Your Mind (1981)Google Scholar
  2. 2.
    Robertson, K.: Strategically desirable brand name characteristics. J. Consum. Mark. 6, 61–71 (1989)CrossRefGoogle Scholar
  3. 3.
    Yorkston, E., Menon, G.: A sound idea: phonetic effects of brand names on consumer judgments. J. Consum. Res. 31, 43–51 (2004)CrossRefGoogle Scholar
  4. 4.
    Little, G., Chilton, L.B., Goldman, M., Miller, R.C.: Exploring iterative and parallel human computation processes. In: Proceedings of the ACM SIGKDD Workshop on Human Computation, pp. 68–76. ACM (2010)Google Scholar
  5. 5.
    Clements, J.: Generating 56-bit passwords using markov models (and charles dickens). arXiv preprint arXiv:1502.07786 (2015)
  6. 6.
    Allbery, B.: PWGEN-random but pronounceable password generator. USENET posting in comp. sources. misc (1988)Google Scholar
  7. 7.
    Crawford, H., Aycock, J.: Kwyjibo: automatic domain name generation. Softw. Pract. Exp. 38, 1561–1567 (2008)Google Scholar
  8. 8.
    Bauer, L.: English Word-Formation. Cambridge University Press, Cambridge (1983)Google Scholar
  9. 9.
    Kondrak, G.: Phonetic alignment and similarity. Comput. Humanit. 37, 273–291 (2003)CrossRefGoogle Scholar
  10. 10.
    Hedlund, G.J., Maddocks, K., Rose, Y., Wareham, T.: Natural language syllable alignment: from conception to implementation. In: Proceedings of the Fifteenth Annual Newfoundland Electrical and Computer Engineering Conference (2005)Google Scholar
  11. 11.
    Özbal, G., Strapparava, C.: A computational approach to the automation of creative naming. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, pp. 703–711. Association for Computational Linguistics (2012)Google Scholar
  12. 12.
    Özbal, G., Strapparava, C., Guerini, M.: Brand pitt: a corpus to explore the art of naming. In: Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC-2012), Istanbul, Turkey, May, Citeseer (2012)Google Scholar
  13. 13.
    Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media, Inc., Beijing (2009)Google Scholar
  14. 14.
    Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1, pp. 173–180. Association for Computational Linguistics (2003)Google Scholar
  15. 15.
    Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38, 39–41 (1995)CrossRefGoogle Scholar
  16. 16.
    Goldhahn, D., Eckart, T., Quasthoff, U.: Building large monolingual dictionaries at the leipzig corpora collection: from 100 to 200 languages. In: LREC, pp. 759–765 (2012)Google Scholar
  17. 17.
    Kincaid, Jr., J.P., Rogers, R.L., Chissom, B.S.: Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel. Technical report, DTIC Document (1975)Google Scholar
  18. 18.
    Schiavoni, S., Maggi, F., Cavallaro, L., Zanero, S.: Phoenix: DGA-based botnet tracking and intelligence. In: Dietrich, S. (ed.) DIMVA 2014. LNCS, vol. 8550, pp. 192–211. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-08509-8_11CrossRefGoogle Scholar
  19. 19.
    Webster, M.: The Official Scrabble Players Dictionary. Springfield, USA (2005)Google Scholar
  20. 20.
    Chen, S.F., Goodman, J.: An empirical study of smoothing techniques for language modeling. In: Proceedings of the 34th Annual Meeting on Association for Computational Linguistics, pp. 310–318. Association for Computational Linguistics (1996)Google Scholar
  21. 21.
    Danescu-Niculescu-Mizil, C., Cheng, J., Kleinberg, J., Lee, L.: You had me at hello: how phrasing affects memorability. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, pp. 892–901. Association for Computational Linguistics (2012)Google Scholar
  22. 22.
    Michel, J.B., et al.: Quantitative analysis of culture using millions of digitized books. Science 331, 176–182 (2011)CrossRefGoogle Scholar
  23. 23.
    Joachims, T.: Optimizing search engines using click through data. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 133–142. ACM (2002)Google Scholar
  24. 24.
    Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Developement in Information Retrieval, pp. 335–336. ACM (1998)Google Scholar
  25. 25.
    Modani, N., Khabiri, E., Srinivasan, H., Caverlee, J.: Creating diverse product review summaries: a graph approach. In: Wang, J., Cellary, W., Wang, D., Wang, H., Chen, S.-C., Li, T., Zhang, Y. (eds.) WISE 2015. LNCS, vol. 9418, pp. 169–184. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-26190-4_12CrossRefGoogle Scholar
  26. 26.
    Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. (TOIS) 20, 422–446 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Gaurush Hiranandani
    • 1
    Email author
  • Pranav Maneriker
    • 1
  • Harsh Jhamtani
    • 2
  1. 1.Adobe ResearchBangaloreIndia
  2. 2.Language Technology InstituteCarnegie Mellon UniversityPittsburghUSA

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