Advertisement

Ballpark Learning: Estimating Labels from Rough Group Comparisons

  • Tom HopeEmail author
  • Dafna Shahaf
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9852)

Abstract

We are interested in estimating individual labels given only coarse, aggregated signal over the data points. In our setting, we receive sets (“bags”) of unlabeled instances with constraints on label proportions. We relax the unrealistic assumption of known label proportions, made in previous work; instead, we assume only to have upper and lower bounds, and constraints on bag differences. We motivate the problem, propose an intuitive formulation and algorithm, and apply our methods to real-world scenarios. Across several domains, we show how using only proportion constraints and no labeled examples, we can achieve surprisingly high accuracy. In particular, we demonstrate how to predict income level using rough stereotypes and how to perform sentiment analysis using very little information. We also apply our method to guide exploratory analysis, recovering geographical differences in twitter dialect.

Keywords

Robust Optimization Twitter User Pairwise Constraint Multiple Instance Learn Label Instance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors thank the anonymous reviewers and Ami Wiesel for their helpful comments. Dafna Shahaf is a Harry&Abe Sherman assistant professor, and is supported by ISF grant 1764/15 and Alon grant.

References

  1. 1.
  2. 2.
  3. 3.
    Aggarwal, C.C., Zhai, C.X.: A survey of text clustering algorithms. In: Aggarwal, C.C., Zhai, C.X. (eds.) Mining Text Data, pp. 77–128. Springer, New York (2012)CrossRefGoogle Scholar
  4. 4.
    Ben-Tal, A., El Ghaoui, L., Nemirovski, A.: Robust Optimization. Princeton University Press, Princeton (2009)CrossRefzbMATHGoogle Scholar
  5. 5.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)zbMATHGoogle Scholar
  6. 6.
    Chapelle, O., Schölkopf, B., Zien, A., et al.: Semi-supervised Learning. MIT Press, Cambridge (2006)CrossRefGoogle Scholar
  7. 7.
    Cheplygina, V., Tax, D., Loog, M.: On classification with bags, groups, sets. arXiv preprint arXiv:1406.0281 (2014)
  8. 8.
    Druck, G., Mann, G., McCallum, A.: Learning from labeled features using generalized expectation criteria. In: SIGIR 2008, pp. 595–602 (2008)Google Scholar
  9. 9.
    Eisenstein, J., Brendan, O., Smith, N., Xing, E.P.: A latent variable model for geographic lexical variation. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Cambridge, MA (2010)Google Scholar
  10. 10.
    Eisenstein, J., Smith, N.A., Xing, E.P.: Discovering sociolinguistic associations with structured sparsity. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (2011)Google Scholar
  11. 11.
    Fellbaum, C.: WordNet: An Electronic Lexical Database. Bradford Books (1998)Google Scholar
  12. 12.
    Foulds, J., Frank, E.: A review of multi-instance learning assumptions. Knowl. Eng. Rev. 25, 125 (2010)CrossRefGoogle Scholar
  13. 13.
    Hu, Y., Li, M., Yu, N.: Multiple-instance ranking: learning to rank images for image retrieval. In: Proceedings of CVPR, p. 18 (2008)Google Scholar
  14. 14.
    Joachims, T.: Transductive inference for text classification using support vector machines. In: ICML 1999, pp. 200–209 (1999)Google Scholar
  15. 15.
    Kotzias, D., Denil, M., de Freitas, N., Smyth, P.: From group to individual labels using deep features. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015 (2015)Google Scholar
  16. 16.
    Li, L., Jin, X., Pan, S.J., Sun, J.-T.: Multi-domain active learning for text classification. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1086–1094. ACM (2012)Google Scholar
  17. 17.
    Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity. In: Proceedings of ACL, pp. 271–278 (2004)Google Scholar
  18. 18.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  19. 19.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Quadrianto, N., Smola, A.J., Caetano, T.S., Le, Q.V.: Estimating labels from label proportions. J. Mach. Learn. Res. 10, 2349–2374 (2009)MathSciNetzbMATHGoogle Scholar
  21. 21.
    Rueping, S.: SVM classifier estimation from group probabilities. In: Proceedings of the 27th International Conference on Machine Learning (ICML 2010) (2010)Google Scholar
  22. 22.
    Settles, B.: Active learning literature survey. University of Wisconsin, Madison, 52(55-66):11Google Scholar
  23. 23.
    Settles, B.: Closing the loop: fast, interactive semi-supervised annotation with queries on features and instances. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1467–1478 (2011)Google Scholar
  24. 24.
    Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2, 45–66 (2002)zbMATHGoogle Scholar
  25. 25.
    Wager, S., Blocker, A., Cardin, N.: Weakly supervised clustering: learning fine-grained signals from coarse labels. Ann. Appl. Stat. 9(2), 801–820 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Xing, E.P., Jordan, M.I., Russell, S.J., Ng, A.Y.: Distance metric learning with application to clustering with side-information. In: NIPS 2003. MIT Press (2003)Google Scholar
  27. 27.
    Yu, F., Liu, D., Kumar, S., Jebara, T., Chang, S.: \(\propto \)-SVM for learning with label proportions. In: ICML 2013 (2013)Google Scholar
  28. 28.
    Zhou, G.-T., Lan, T., Vahdat, A., Mori, G.: Latent maximum margin clustering. In: Burges, C., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K. (eds.) Advances in Neural Information Processing Systems 26, pp. 28–36 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.The Hebrew University of JerusalemJerusalemIsrael

Personalised recommendations