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Soft Computing

, Volume 22, Issue 24, pp 8107–8118 | Cite as

Identifying intention posts in discussion forums using multi-instance learning and multiple sources transfer learning

  • Hyun-Je Song
  • Seong-Bae Park
Methodologies and Application
  • 136 Downloads

Abstract

This paper proposes a novel method for identifying intention posts in discussion forums. The main problem of identifying intention posts in discussion forums is that there exist a few intention sentences even in a post expressing an intention. That is, an intention post consists of a few intention sentences and a number of non-intention sentences, while non-intention posts have only non-intention sentences. Therefore, multi-instance learning which regards a post as a bag and the sentences in the post as instances of the bag is adopted as a solution to this problem. One distinct characteristic of the posts is that the ways of expressing an intention are similar across domains. Thus, we incorporate a multiple sources transfer learning into the multi-instance learning. As a result, the multi-instance learning is enhanced by leveraging knowledge of expressing intentions from multiple source domains. Through a set of experiments, it is proven that the proposed method is effective at identifying intention posts in discussion forums.

Keywords

Intention posts identification Multi-instance learning Multiple sources transfer learning Classification in discussion forums 

Notes

Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (No. 2016R1D1A1B04935678).

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

  1. Alguliev RM, Aliguliyev RM, Mehdiyev CA (2011) Sentence selection for generic document summarization using an adaptive differential evolution algorithm. Swarm Evol Comput 1(4):213–222CrossRefGoogle Scholar
  2. Andrews S, Tsochantaridis I, Hofmann T (2003) Support vector machines for multiple-instance learning. In: Advances in neural information processing systems 15, pp 561–568Google Scholar
  3. Aue A, Gamon M (2005) Customizing sentiment classifiers to new domains: a case study. In: Proceedings of recent advances in natural language processingGoogle Scholar
  4. Banerjee N, Chakraborty D, Joshi A, Mittal S, Rai A, Ravindran B (2012) Towards analyzing micro-blogs for detection and classification of real-time intentions. In: Proceedings of the sixth international AAAI conference on weblogs and social media, pp 391–394Google Scholar
  5. Bin G, Sheng VS (2017) A robust regularization path algorithm for \(nu\)-support vector classification. IEEE Trans Neural Netw Learn Syst 28(5):1241–1248CrossRefGoogle Scholar
  6. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th annual conference on computational learning theory, pp 92–100Google Scholar
  7. Bunescu RC, Mooney RJ (2007) Multiple instance learning for sparse positive bags. In: Proceedings of the 24th international conference on Machine learning, pp 105–112Google Scholar
  8. Cauwenberghs G, Poggio T (2000) Incremental and decremental support vector machine learning. In: Advances in neural information processing systems, vol 13, pp 409–415Google Scholar
  9. Chen M, Weinberger KQ, Blitzer J (2011) Co-training for domain adaptation. In: Advances in Neural Information Processing Systems 24. Curran Associates, Inc., pp 2456–2464Google Scholar
  10. Chen Z, Liu B, Hsu M, Castellanos M, Ghosh R (2013) Identifying intention posts in discussion forums. In: Proceedings of the 2013 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 1041–1050Google Scholar
  11. Clarke J, Lapata M (2007) Modelling compression with discourse constraints. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning, pp 1–11Google Scholar
  12. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297zbMATHGoogle Scholar
  13. Dai W, Yang Q, Xue GR, Yu Y (2007) Boosting for transfer learning. In: Proceedings of the 24th international conference on Machine learning, ACM, pp 193–200Google Scholar
  14. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc 39(1):1–38MathSciNetzbMATHGoogle Scholar
  15. Dietterich TG, Lathrop RH, Lozano-Pérez T (1997) Solving the multiple-instance problem with axis-parallet rectangles. Artif Intell 89(1–2):31–71CrossRefGoogle Scholar
  16. Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9(Aug):1871–1874zbMATHGoogle Scholar
  17. Ganu G, Marian A (2013) One size does not fit all: multi-granularity search of web forums. In: Proceedings of the 22nd ACM international conference on information and knowledge management, pp 9–18Google Scholar
  18. Gärtner T, Flach PA, Kowalczyk A, Smola AJ (2002) Multi-instance kernels. In: Proceedings of the 19th international conference on machine learning, pp 179–186Google Scholar
  19. Gu B, Sheng VS, Li S (2015a) Bi-parameter space partition for cost-sensitive svm. In: Proceedings of the 24th international joint conference on artificial intelligence, pp 3532–3539Google Scholar
  20. Gu B, Sheng VS, Tay KY, Romano W, Li S (2015b) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416MathSciNetCrossRefGoogle Scholar
  21. Gu B, Sheng VS, Wang Z, Ho D, Osman S, Li S (2015c) Incremental learning for \(\nu \)-support vector regression. Neural Netw 67:140–150CrossRefGoogle Scholar
  22. Gu B, Sun X, Sheng VS (2016) Structural minimax probability machine. IEEE Trans Neural Netw Learn Syst PP(99):1–11Google Scholar
  23. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1):389–422CrossRefGoogle Scholar
  24. Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. Mach Learn ECML-98: 137–142Google Scholar
  25. Joachims T (2006) Training linear svms in linear time. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 217–226Google Scholar
  26. Katsumata S, Takeda A (2015) Robust cost sensitive support vector machine. In: Proceedings of the 18th international conference on artificial intelligence and statistics, pp 434–443Google Scholar
  27. Kim HD, Zhai C (2009) Generating comparative summaries of contradictory opinions in text. In: Proceedings of the 18th ACM international conference on information and knowledge management, pp 385–394Google Scholar
  28. Knight K, Marcu D (2002) Summarization beyond sentence extraction: a probabilistic approach to sentence compression. Artif Intell 139(1):91–107CrossRefGoogle Scholar
  29. Kong Y, Zhang M, Ye D (2017) A belief propagation-based method for task allocation in open and dynamic cloud environments. Knowl Based Syst 115:123–132CrossRefGoogle Scholar
  30. Leopold E, Kindermann J (2002) Text categorization with support vector machines. How to represent texts in input space? Mach Learn 46(1–3):423–444CrossRefGoogle Scholar
  31. Li C, Du Y, Liu J, Zheng H, Wang S (2016) A novel approach of identifying user intents in microblog. In: Proceedings of the 12th international conference on intelligent computing methodologies, pp 391–400Google Scholar
  32. Lin H, Bilmes J (2011) A class of submodular functions for document summarization. In: Proceedings of the 49th annual meeting of the association for computational linguistics, pp 510–520Google Scholar
  33. Luo P, Zhuang F, Xiong H, Xiong Y, He Q (2008) Transfer learning from multiple source domains via consensus regularization. In: Proceedings of the 17th ACM conference on information and knowledge management, pp 103–112Google Scholar
  34. Maron O, Lozano-Pérez T (1998) A framework for multiple-instance learning. In: Advances in neural information processing systems, pp 570–576Google Scholar
  35. Maron O, Ratan AL (1998) Multiple-instance learning for natural scene classification. In: Proceedings of the 15th international conference on machine learning, pp 341–349Google Scholar
  36. Masnadi-Shirazi H, Vasconcelos N (2010) Risk minimization, probability elicitation, and cost-sensitive svms. In: Proceedings of the 27th international conference on machine learning, pp 759–766Google Scholar
  37. McClosky D, Charniak E, Johnson M (2006) Reranking and self-training for parser adaptation. In: Proceedings of the 21st international conference on computational linguistics and the 44th annual meeting of the association for computational linguistics, pp 337–344Google Scholar
  38. McDonald R (2007) A study of global inference algorithms in multi-document summarization. In: Proceedings of the 29th European conference on IR research, pp 557–564Google Scholar
  39. Mihalcea R, Tarau P (2004) Textrank: bringing order into texts. In: Proceedings of the 2004 conference on empirical methods in natural language processing, pp 404–411Google Scholar
  40. Nishikawa H, Hasegawa T, Matsuo Y, Kikui G (2010) Opinion summarization with integer linear programming formulation for sentence extraction and ordering. In: Proceedings of the 23rd international conference on computational linguistics, pp 910–918Google Scholar
  41. Pan Z, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcast 61(2):166–176Google Scholar
  42. Papadimitriou D, Koutrika G, Velegrakis Y, Mylopoulos J (2017) Finding related forum posts through content similarity over intention-based segmentation. IEEE Trans Knowl Data Eng PP(99):1–1Google Scholar
  43. Qazvinian V, Radev DR (2010) Identifying non-explicit citing sentences for citation-based summarization. In: Proceedings of the 48th annual meeting of the association for computational linguistics, pp 555–564Google Scholar
  44. Ren Z, Ma J, Wang S, Liu Y (2011) Summarizing web forum threads based on a latent topic propagation process. In: Proceedings of the 20th ACM international conference on information and knowledge management, pp 879–884Google Scholar
  45. Scholkopf B, Smola AJ (2001) Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, CambridgeGoogle Scholar
  46. Schölkopf B, Smola AJ, Williamson RC, Bartlett PL (2000) New support vector algorithms. Neural Comput 12(5):1207–1245CrossRefGoogle Scholar
  47. Sondhi P, Gupta M, Zhai C, Hockenmaier J (2010) Shallow information extraction from medical forum data. In: Proceedings of the 23rd international conference on computational linguistics, pp 1158–1166Google Scholar
  48. Surdeanu M, Tibshirani J, Nallapati R, Manning CD (2012) Multi-instance multi-label learning for relation extraction. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, pp 455–465Google Scholar
  49. Tan B, Zhong E, Xiang EW, Yang Q (2013) Multi-transfer: transfer learning with multiple views and multiple sources. In: Proceedings of the 13th SIAM international conference on data mining, pp 243–251CrossRefGoogle Scholar
  50. Tian Q, Chen S (2017) Cross-heterogeneous-database age estimation through correlation representation learning. Neurocomputing 238:286–295CrossRefGoogle Scholar
  51. Wang J, Zucker JD (2000) Solving the multiple-instance problem: a lazy learning approach. In: Proceedings of the 17th international conference on machine learning, pp 1119–1125Google Scholar
  52. Wang Q, Ruan L, Si L (2014) Adaptive knowledge transfer for multiple instance learning in image classification. In: Proceedings of 28th AAAI conference on artificial intelligence, pp 1334–1340Google Scholar
  53. Xue H, Chen S, Yang Q (2011) Structural regularized support vector machine: a framework for structural large margin classifier. IEEE Trans Neural Netw 22(4):573–587CrossRefGoogle Scholar
  54. Xue Y, Jiang J, Zhao B, Ma T (2017) A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Comput 1–18Google Scholar
  55. Yang Y, Pedersen JO (1997) A comparative study on feature selection in text categorization. In: Proceedings of the 14th international conference on machine learning, pp 412–420Google Scholar
  56. Yao Y, Doretto G (2010) Boosting for transfer learning with multiple sources. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1855–1862Google Scholar
  57. Yeung DS, Wang D, Ng WW, Tsang EC, Wang X (2007) Structured large margin machines: sensitive to data distributions. Mach Learn 68(2):171–200CrossRefGoogle Scholar
  58. Zhang WJ, Zhou ZH (2014) Multi-instance learning with distribution change. In: Proceedings of 28th AAAI conference on artificial intelligence, pp 2184–2190Google Scholar
  59. Zhang Y, Sun X, Wang B (2016) Efficient algorithm for k-barrier coverage based on integer linear programming. China Commun 13(7):16–23CrossRefGoogle Scholar
  60. Zhou ZH, Xu JM (2007) On the relation between multi-instance learning and semi-supervised learning. In: Proceedings of the 24th international conference on machine learning, pp 1167–1174Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringKyungpook National UniversityDaeguKorea

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