Have a personal or library account? Click to login
An English Neural Network that Learns Texts, Finds Hidden Knowledge, and Answers Questions Cover

An English Neural Network that Learns Texts, Finds Hidden Knowledge, and Answers Questions

By: Yuanzhi Ke and  Masafumi Hagiwara  
Open Access
|May 2017

References

  1. [1] E. Brill, A simple rule-based part of speech tagger, In Proceedings of the Workshop on Speech and Natural Language, HLT’91, pp. 112-116, Association for Computational Linguistics, Stroudsburg, PA, USA, 199210.3115/1075527.1075553
  2. [2] C. D. Manning and H. Sch¨utze, Foundations of statistical natural language processing, MIT press, 1999
  3. [3] C. E. Shannon, A mathematical theory of communication, SIGMOBILE Mob. Comput. Commun. Rev., vol. 5, no. 1, pp. 3-55, 200110.1145/584091.584093
  4. [4] N. Chomsky, Three models for the description of language, Information Theory, IRE Transactions on, vol. 2, no. 3, pp.113-124, 195610.1109/TIT.1956.1056813
  5. [5] W. A. Gale, K. W. Church, and D. Yarowsky, Work on statistical methods for word sense disambiguation, In Working Notes of the AAAI Fall Symposium on Probabilistic Approaches to Natural Language, vol. 54, p. 60. 1992
  6. [6] J. Kupiec, Robust part-of-speech tagging using a hidden markov model, Computer Speech & Language, vol. 6, no. 3, pp. 225 - 242, 199210.1016/0885-2308(92)90019-Z
  7. [7] H. Schmid, Probabilistic part-of-speech tagging using decision trees, in Proceedings of the international conference on new methods in language processing, vol. 12, pp. 44-49. Citeseer, 1994
  8. [8] A. Ratnaparkhi et al, A maximum entropy model for part-of-speech tagging, in Proceedings of the conference on empirical methods in natural language processing, vol. 1, pp. 133-142. Philadelphia, USA, 1996
  9. [9] P. F. Brown, V. J. D. Pietra, S. A. D. Pietra, and R. L. Mercer, The mathematics of statistical machine translation: Parameter estimation, Computational linguistics, vol. 19, no. 2, pp. 263-311, 1993
  10. [10] H. Hotta, M. Kittaka, and M. Hagiwara,Word vectorization using relations among words for neural network, IEEJ Transactions on Electronics, Information and Systems, vol. 130, pp. 75-82, 201010.1541/ieejeiss.130.75
  11. [11] G. Tsatsaronis, I. Varlamis, and M. Vazirgiannis, Text relatedness based on a word thesaurus, Journal of Artificial Intelligence Research, vol. 37, no. 1, pp. 1-40, 201010.1613/jair.2880
  12. [12] G. Tsatsaronis, I. Varlamis, and M. Vazirgiannis, Word sense disambiguation with semantic networks, In Text, Speech and Dialogue, pp. 219-226. Springer, 200810.1007/978-3-540-87391-4_29
  13. [13] R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, and P. Kuksa, Natural language processing (almost) from scratch, The Journal of Machine Learning Research, vol. 12, pp. 2493-2537, 2011
  14. [14] T. Sagara and M. Hagiwara, Natural language neural network and its application to questionanswering system, Neurocomputing, vol. 142, pp. 201 - 208, 201410.1016/j.neucom.2014.04.048
  15. [15] L. Dong, F. Wei, M. Zhou, and K. Xu, Question answering over Freebase with multi-column convolutional neural networks, in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp. 260-269. Association for Computational Linguistics, Beijing, China, July 201510.3115/v1/P15-1026
  16. [16] D. Bahdanau, K. Cho, and Y. Bengio, Neural machine translation by jointly learning to align and translate, CoRR, vol. abs/1409.0473, 2014
  17. [17] J. E. Hummel and K. J. Holyoak, Distributed representations of structure: A theory of analogical access and mapping, Psychological Review, vol. 104, no. 3, p. 427, 199710.1037/0033-295X.104.3.427
  18. [18] J. E. Hummel and K. J. Holyoak, A symbolicconnectionist theory of relational inference and generalization, Psychological review, vol. 110, no. 2, p. 220, 200310.1037/0033-295X.110.2.220
  19. [19] J. E. Hummel and K. J. Holyoak, Relational reasoning in a neurally plausible cognitive architecture an overview of the LISA project, Current Directions in Psychological Science, vol. 14, no. 3, pp. 153-157, 200510.1111/j.0963-7214.2005.00350.x
  20. [20] M. Saito and M. Hagiwara, Natural language processing neural network for analogical inference, In The 2010 International Joint Conference on Neural Networks, pp.1-7, 201010.1109/IJCNN.2010.5596742
  21. [21] T. Kudo and H. Kazawa, Web Japanese N-gram version 1, Gengo Shigen Kyokai, vol. 14, 2007
  22. [22] M. Fukuda, S. Nobesawa, and I. Tahara, Knowledge representation for query-answer, In Forum on Information Technology, vol. 3, pp. 233-236, Information Processing Society of Japan, 2004
  23. [23] S. Ikehara, M. Miyazaki, S. Shirai, A. Yokoo, H. Nakaiwa, K. Ogura, Y. Ooyama, and Y. Hayashi, GoiTaikei-A Japanese Lexicon, Iwanami Shoten, 1997
  24. [24] T. Kudo, K. Yamamoto, and Y. Matsumoto, Applying conditional random fields to Japanese morphological analysis, in Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, vol. 2004, pp. 230-237. 2004
  25. [25] G. A. Miller, WordNet: a lexical database for english, Communications of the ACM, vol. 38, no. 11, pp. 39-41, 199510.1145/219717.219748
  26. [26] G. Miller and C. Fellbaum, WordNet: An electronic lexical database, 199810.7551/mitpress/7287.001.0001
  27. [27] M. P. Marcus, M. A. Marcinkiewicz, and B. Santorini, Building a large annotated corpus of english: The Penn Treebank, Comput. Linguist., vol. 19, no. 2, pp. 313-330, June 199310.21236/ADA273556
  28. [28] M. Marcus, G. Kim, M. A. Marcinkiewicz, R. MacIntyre, A. Bies, M. Ferguson, K. Katz, and B. Schasberger, The Penn Treebank: Annotating predicate argument structure, in Proceedings of the Workshop on Human Language Technology, HLT ’94, pp. 114-119, Association for Computational Linguistics, Stroudsburg, PA, USA, 199410.3115/1075812.1075835
  29. [29] P. K. Martha and M. Palmer, From Treebank to Propbank, in Proceedings of the International Conference on Language Resources and Evaluation 2002, Las Palmas, Canary Islands, Spain, 2002
  30. [30] P. Kingsbury, M. Palmer, and M. Marcus, Adding semantic annotation to the penn treebank, in Proceedings of the Human Language Technology Conference, pp. 252-256, Citeseer, 2002
  31. [31] M. Palmer, D. Gildea, and P. Kingsbury, The proposition bank: An annotated corpus of semantic roles, Comput. Linguist., vol. 31, no. 1, pp. 71-106, March 200510.1162/0891201053630264
  32. [32] P. E.Woodford, The test of english for international communication (TOEIC), 1980
  33. [33] National Institute of Standards and Technology, NIST TREC Document Database: Disk 4, Retrieved June 25, 2016, from http://www.nist.gov/srd/nistsd22.cfm
  34. [34] National Institute of Standards and Technology, NIST TREC Document Database: Disk 5, Retrieved June 25, 2016, from http://www.nist.gov/srd/nistsd23.cfm
  35. [35] E. M. Voorhees et al. The TREC-8 question answering track report, in Proceedings of the 8th Text Retreval Conference, vol. 99, pp.77-82. NIST, Gaithersburg, MD, 1999
  36. [36] L. Loungheed, Longman preparation series for the new TOEIC test: More practice tests, 2006
  37. [37] T. S. Committee et al., TOEIC program data & analysis, 2014
  38. [38] E. T. Service, Examinee handbook listening & reading, 2008
Language: English
Page range: 229 - 242
Submitted on: May 18, 2016
Accepted on: Sep 14, 2016
Published on: May 3, 2017
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2017 Yuanzhi Ke, Masafumi Hagiwara, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.