Memetic Algorithm Based on Global-Best Harmony Search and Hill Climbing for Part of Speech Tagging

1. Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. Pearson - Addison Wesley, New York (1999)

   Google Scholar 

2. Sammut, C., Webb, G.I. (eds.): Encyclopedia of Machine Learning (Part of Speech Tagging). Springer, New York (2010)

   Google Scholar 

3. Paul, A., Purkayastha, B.S., Sarkar, S.I.: Hidden Markov model based part of speech tagging for Nepali language. In: 2015 International Symposium on Advanced Computing and Communication (lSACC), Silchar, pp. 149–156 (2015)

   Google Scholar 

4. Makazhanov, A., Yessenbayev, Z., Sabyrgaliyev, I., Sharafudinov, A.: On certain aspects of Kazakh part-of-speech. In: IEEE 8th International Conference on Application of Information and Communication Technologies (AICT), Astana, pp. 1–4 (2014)

   Google Scholar 

5. Ratnaparkhi, A.: A maximum entropy model for part-of-speech tagging. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 113–142 (1996)

   Google Scholar 

6. Ariaratnam, I., Weerasinghe, A.R., Liyanage, C.: A shallow parser for Tamil. In: 2014 International Conference on Advances in ICT for Emerging Regions (ICTer), Colombo, pp. 197–203 (2014)

   Google Scholar 

7. Brants, T.: TnT - a statistical part-of-speech tagger. In: Proceedings of the Sixth Conference on Applied Natural Language Processing, ANLC 2000, Stroudsburg, PA, USA, pp. 224–231 (2000)

   Google Scholar 

8. Brill, E.: A simple rule-based part of speech tagger. In: Proceedings of the Third Conference on Applied Natural Language Processing, ANLC 1992, Stroudsburg, PA, USA, pp. 152–155 (1992)

   Google Scholar 

9. Brill, E.: Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging. Comput. Linguist. 21(4), 543–565 (1995)

   MathSciNet  Google Scholar 

10. Schmid, H.: Part-of-speech tagging with neural networks. In: Proceedings of the 15th Conference on Computational Linguistics, Stroudsburg, PA, USA, pp. 172–176 (1994)

    Google Scholar 

11. Nakamura, M., Shikano, K.: A study of English word category prediction based on neutral networks, acoustics, speech, and signal processing. In: International Conference on Acoustics, Speech, and Signal Processing, IEEE, Glasgow, pp. 731–734 (1989)

    Google Scholar 

12. Forsati, R., Shamsfard, M., Mojtahedpour, P.: An efficient meta heuristic algorithm for POS-tagging. In: 2010 Fifth International Multi-Conference on Computing in the Global Information Technology (ICCGI), Valencia (2010)

    Google Scholar 

13. Forsati, R., Shamsfard, M.: Novel harmony search-based algorithms for part-of-speech tagging. Knowl. Inf. Syst. 42(3), 709–736 (2015)

    Article  Google Scholar 

14. Silva, A.P., Silva, A., Rodríguez, I.: An approach to the POS tagging problem using genetic algorithms. In: Madani, K., Correia, A., Rosa, A., Filipe, J. (eds.) Computational Intelligence. Studies in Computational Intelligence, vol. 577, pp. 3–17. Springer, Cham (2012). https://doi.org/10.1007/978-3-319-11271-8_1

    Google Scholar 

15. Jianchao, T.: An English part of speech tagging method based on maximum entropy. In: 2015 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Halong Bay, Vietnam, pp. 76–80 (2015)

    Google Scholar 

16. Ranjan Das, B., Sahoo, S., Sekhar Panda, C., Patnaik, S.: Part of Speech tagging in Odia using support vector machine. In: Procedia Computer Science, International Conference on Intelligent Computing, Communication Converge, ICCC-2015, vol. 48, pp. 507–512 (2015)

    Google Scholar 

17. Ekbal, A., Bandyopadhyay, S.: Part of speech tagging in Bengali using support vector machine. In: International Conference on Information Technology, ICIT 2008, pp. 10–111 (2008)

    Google Scholar 

18. Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning 2001, pp. 282–289 (2001)

    Google Scholar 

19. Araujo, L.: How evolutionary algorithms are applied to statistical natural language processing. Artif. Intell. Rev. 28(4), 275–303 (2007)

    Article  Google Scholar 

20. AlSuhaibani, R.S., Newman, C.D., Collard, M.L., Maletic, J.I.: Heuristic-based part-of-speech tagging of source code identifiers and comments. In: 2015 IEEE 5th Workshop on Mining Unstructured Data (MUD), Bremen, pp. 1–5 (2015)

    Google Scholar 

21. Aziz, T.A., Sunitha, C.: A hybrid parts of speech tagger for Malayalam. In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Kochi, pp. 1502–1507 (2015)

    Google Scholar 

22. Mall, S., Jaiswal, U.C.: Innovative algorithms for parts of speech tagging in Hindi-English machine. In: 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), Noida, pp. 709–714 (2015)

    Google Scholar 

23. Tian, Y., Lo, D.: A comparative study on the effectiveness of part-of-speech tagging techniques on bug reports. In: 2015 IEEE 22nd International Conference on Software Analysis, Evolution and Reengineering (SANER), Montreal, QB, pp. 570–574 (2015)

    Google Scholar 

24. Carneiro, H.C., França, F.M., Lima, P.M.: Multilingual part-of-speech tagging with weightless neural networks. Neural Netw. 66, 11–21 (2015)

    Article  Google Scholar 

25. Carneiro, H.C., França, F.M., Lima, P.M.: WANN-tagger - a weightless artificial neural network tagger for the Portuguese language. In: Proceedings of the International Conference on Fuzzy Computation and International Conference on Neural Computation, ICFC-ICNC 2010, Valencia, pp. 330–335 (2010)

    Google Scholar 

26. Poel, M., Boschman, E, Akker, R.A.: Neural network based Dutch part of speech tagger. In: Proceedings of the Twentieth Belgian-Dutch Artificial Intelligence Conference, BNAIC 2008, The Netherlands, pp. 217–224 (2008)

    Google Scholar 

27. Zennaki, O., Semmar, N., Besacier, L.: Unsupervised and lightly supervised part-of-speech tagging using recurrent neural networks. In: 29th Pacific Asian Conference on Language, Information and Computation, Shangai, China, pp. 133–142 (2015)

    Google Scholar 

28. Duong, L., Cohn, T., Verspoor, K., Bird, S., Cook, P.: What a can we get from 1000 tokens? A case study of multilingual POS tagging for resource-poor languages. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, pp. 886–897 (2014)

    Google Scholar 

29. Forsati, R., Shamsfard, M.: Cooperation of evolutionary and statistical statistical POS-tagging. In: 2012 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), Shiraz, Fars, pp. 446–451 (2012)

    Google Scholar 

30. Silva, A.P., Silva, A., Rodríguez, I.: Part-of-speech tagging using evolutionary computation. In: Terrazas, G., Otero, F., Masegosa, A. (eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2013). Studies in Computational Intelligence, vol. 512, pp. 167–178. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-01692-4_13

    Chapter  Google Scholar 

31. Silva, A.P., Silva, A., Rodríguez, I.: Tagging with disambiguation rules a new evolutionary approach to the part-of-speech tagging problem. In: Proceedings of the 4th International Joint Conference on Computational Intelligence, ECTA-2012, pp. 5–14 (2012)

    Google Scholar 

32. Silva, A.P., Silva, A., Rodríguez, I.: A new approach to the POS tagging problem using evolutionary. In: Proceedings of Recent Advances in Natural Language Processing, Hissar, Bulgaria, pp. 619–625 (2013)

    Google Scholar 

33. Silva, A.P., Silva, A., Rodríguez, I.: PSO-tagger: a new biologically inspired approach to the part-of-speech tagging problem. In: Tomassini, M., Antonioni, A., Daolio, F., Buesser, P. (eds.) ICANNGA 2013. LNCS, vol. 7824, pp. 90–99. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37213-1_10

    Chapter  Google Scholar 

34. Bachir Menai, M.E.: Word sense disambiguation using evolutionary algorithms – application to Arabic language. Comput. Hum. Behav. 41, 92–103 (2014)

    Article  Google Scholar 

35. Ekbal, A., Saha, S.: Simulated annealing based classifier ensemble techniques: application to part of speech tagging. Inf. Fusion 14(3), 288–300 (2013)

    Article  Google Scholar 

36. Ekbal, A., Saha, S.: A multiobjective simulated annealing approach for classifier ensemble: named entity recognition in Indian languages as case studies. Expert Syst. Appl. 38, 14760–14772 (2011)

    Article  Google Scholar 

37. Dinakaramani, A., Rashel, F., Luthfi, A., Manurung, R.: Designing an Indonesian part of speech tagset and manually tagged Indonesian corpus. In: 2014 International Conference on Asian Language Processing (IALP), Kuching (2014)

    Google Scholar 

38. Petrov, S., Das, D., McDonald, R.: A universal part-of-speech tagset. In: Proceedings of the 8th International Conference on Language Resources and Evaluation, LREC 2012, Istambul (2012)

    Google Scholar 

39. Rabbi, I., Khan, M.A., Ali, R.: Developing a tagset for Pashto part of speech tagging. In: Second International Conference on Electrical Engineering, Lahore (Pakistan) (2008)

    Google Scholar 

40. Francis, W.N., Kucera, H.: Brown Corpus (1979). http://clu.uni.no/icame/manuals/BROWN/INDEX.HTM#bc8. Accessed 21 Nov 2016

41. Marcus, M.P., Marcinkiewicz, M.A., Santorini, B.: Building a large annotated corpus of English: the penn treebank. J. Comput. Linguist. Spec. Issue Using Large Corpora, II 19(2), 313–330 (1993)

    Google Scholar 

42. Koehn, P.: Europarl: a parallel corpus for statistical machine translation. In: Proceedings of the Tenth Machine Translation Summit (MT Summit XX), Phuket, Thailand (2005)

    Google Scholar 

43. Brownlee, J.: Clever algorithms nature-inspired programming recipes. Melbourne, lulu.com (2011)

44. Yang, X.-S.: Harmony search as a metaheuristic algorithm. In: Geem, Z.W. (ed.) Music-Inspired Harmony Search Algorithm. Studies in Computational Intelligence, vol. 191, pp. 1–14. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-00185-7_1

    Chapter  Google Scholar 

45. Lee, K.S., Geem, Z.W.: A new meta-heuristic algorithm for continuos engineering optimization: harmony search theory and practice. Comput. Methods Appl. Mech. Eng. 194, 3902–3933 (2005)

    Article  MATH  Google Scholar 

46. Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188(2), 1567–1579 (2007)

    MathSciNet  MATH  Google Scholar 

47. Omran, M.G., Mahdavi, M.: Global-best harmony search. Appl. Math. Comput. 198, 643–656 (2008)

    MathSciNet  MATH  Google Scholar 

48. Forsati, R., Shamsfard, M.: Hybrid PoS-tagging: a cooperation of evolutionary and statistical approaches. Appl. Math. Model. 38(13), 3193–3211 (2014)

    Article  Google Scholar 

49. NLTK Project: Natural Language Toolkit (2017). http://www.nltk.org/. Accessed 15 June 2017

50. Sierra, L.-M., Cobos, L., Corrales, J.-C.: Continuous optimization based on a hybridization of differential evolution with k-means. In: Bazzan, A.L.C., Pichara, K. (eds.) IBERAMIA 2014. LNCS, vol. 8864, pp. 381–392. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12027-0_31

    Google Scholar 

51. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micromachine and Human Science (1995)

    Google Scholar 

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