Neural Text Normalization in Speech-to-Text Systems with Rich Features

References

• Aw, A., M. Zhang, J. Xiao, and J. Su. 2006. A phrase-based statistical model for SMS text normalization. In The COLING/ACL on Main conference poster sessions. Association for Computational Linguistics, Sydney, Australia, 33–40.
   Google Scholar
• Bach, N. X., N. D. Linh, and T. M. Phuong. 2018. An empirical study on POS tagging for vietnamese social media text. Computer Speech & Language 50:1–15. doi:10.1016/j.csl.2017.12.004.
   Web of Science ®Google Scholar
• Cohen, J. 1960. A coefficient of agreement for nominal scales. Journal Educational and Psychological Measurement 20 (1):37–46. doi:10.1177/001316446002000104.
   Web of Science ®Google Scholar
• Eryigit, G., and D. Torunoglu-selamet. 2017. Social media text normalization for Turkish. Journal of Natural Language Engineering 23 (6):835–75. doi:10.1017/S1351324917000134.
   Web of Science ®Google Scholar
• Hassan, H., and A. Menezes. 2013. Social text normalization using contextual graph random walks. In 51st Annual Meeting of the Association for Computational Linguistics, Bulgaria, vol. 1, 1577–86.
   Google Scholar
• Hochreiter, S., and J. Schmidhuber. 1997. Long short-term memory. Journal Neural Computation 9 (8):1735–80. doi:10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Ikeda, T., H. Shindo, and Y. Matsumoto. 2016. Japanese text normalization with encoder-decoder model. In The COLING 2016 Organizing Committee, Proceedings of the 2nd Workshop on Noisy Usergenerated Text (WNUT), Osaka, Japan, 129–37.
   Google Scholar
• Lafferty, J. D., A. McCallum, and F. C. N. Perera. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In 18th International Conference on Machine Learning, 282–89. San Francisco: Morgan Kaufmann Publishers Inc.
   Google Scholar
• Lample, G., M. Ballesteros, S. Subramanian, K. Kawakami, and C. Dyer. 2016. Neural architectures for named entity recognition. In 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 260–70. San Diego: Association for Computational Linguistics.
   Google Scholar
• LeCun, Y., B. E. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. E. Hubbard, and L. D. Jackel. 1989. Handwritten digit recognition with a back-propagation network. In Advances in Neural Information Processing Systems 2, 396–404. CA, United States: Morgan-Kaufmann Publisher.
   Google Scholar
• Mikolov, T., S. Kombrink, L. Burget, J. Ernock, and S. Khudanpur. 2011. Extensions of recurrent neural network language model. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5528–31. Prague, Czech Republic.
   Google Scholar
• Pennington, J., R. Socher, and C. D. Manning. 2014. Glove: Global vectors for word representation. In The 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), vol 14, 1532–43. Doha, Qatar: Association for Computational Linguistics publisher.
   Google Scholar
• Schutze, H. 1997. Ambiguity Resolution in Language Learning: Computational and Cognitive Models, 176. Stanford, CA : CSLI Publications.
   Google Scholar
• Tran, T. O., and C. T. Luong. 2018. Towards understanding user requests in AI bots. In 15th Pacific Rim International Conference on Artificial Intelligence (PRICAI), 864–77, Nanjing, China.
   Google Scholar
• Yadav, V., and S. Bethard. 2018. A survey on recent advances in named entity recognition from deep learning models. In The 27th International Conference on Computational Linguistics, Santa Fe, New Mexico, USA, 2145–58.
   Google Scholar
• Yolchuyeva, S., B. Gyires-Toth, and G. Nemeth. 2018. Text normalization with convolutional neural networks. International Journal of Speech Technology 21 (4):589–600. doi:10.1007/s10772-018-9521-x.
   Google Scholar
• Zhu, Q., X. Li, A. Conesa, and C. Pereira. 2017. GRAM-CNN: A deep learning approach with local context for named entity recognition in biomedical text. Journal Bioinformatics 34 (9):1547–54. doi:10.1093/bioinformatics/btx815.
   Google Scholar