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Deep Learning and Its Applications to Natural Language Processing

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Deep Learning: Fundamentals, Theory and Applications

Part of the book series: Cognitive Computation Trends ((COCT,volume 2))

Abstract

Natural language processing (NLP), utilizing computer programs to process large amounts of language data, is a key research area in artificial intelligence and computer science. Deep learning technologies have been well developed and applied in this area. However, the literature still lacks a succinct survey, which would allow readers to get a quick understanding of (1) how the deep learning technologies apply to NLP and (2) what the promising applications are. In this survey, we try to investigate the recent developments of NLP, centered around natural language understanding, to answer these two questions. First, we explore the newly developed word embedding or word representation methods. Then, we describe two powerful learning models, Recurrent Neural Networks and Convolutional Neural Networks. Next, we outline five key NLP applications, including (1) part-of-speech tagging and named entity recognition, two fundamental NLP applications; (2) machine translation and automatic English grammatical error correction, two applications with prominent commercial value; and (3) image description, an application requiring technologies of both computer vision and NLP. Moreover, we present a series of benchmark datasets which would be useful for researchers to evaluate the performance of models in the related applications.

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Notes

  1. 1.

    https://code.google.com/archive/p/word2vec/

  2. 2.

    https://radimrehurek.com/gensim/

  3. 3.

    https://nlp.stanford.edu/projects/glove/

  4. 4.

    http://www.nltk.org/

  5. 5.

    https://nlp.stanford.edu/software/tagger.shtml

  6. 6.

    https://opennlp.apache.org/

  7. 7.

    http://wiki.languagetool.org/developing-a-disambiguator

  8. 8.

    https://aclweb.org/aclwiki/POS_Tagging_(State_of_the_art)

  9. 9.

    The source code can be found at https://github.com/rsennrich/nematus.

  10. 10.

    https://www.grammarly.com/

  11. 11.

    https://languagetool.org/

  12. 12.

    https://incubator.apache.org/wave/

  13. 13.

    Ginger

  14. 14.

    https://code.google.com/archive/p/word2vec/

  15. 15.

    https://nlp.stanford.edu/projects/glove/

  16. 16.

    http://storage.googleapis.com/books/ngrams/books/datasetsv2.html

  17. 17.

    https://catalog.ldc.upenn.edu/ldc2006t13

  18. 18.

    http://trec.nist.gov/data/reuters/reuters.html

  19. 19.

    http://ai.stanford.edu/~amaas/data/sentiment/

  20. 20.

    http://www.cs.cornell.edu/people/pabo/movie-review-data/

  21. 21.

    https://catalog.ldc.upenn.edu/ldc99t42

  22. 22.

    https://catalog.ldc.upenn.edu/LDC2000T43

  23. 23.

    http://www.statmt.org/europarl/

  24. 24.

    https://conferences.unite.un.org/uncorpus

  25. 25.

    http://www.comp.nus.edu.sg/~nlp/conll14st.html

  26. 26.

    http://textmining.lt/aesw/index.html

  27. 27.

    http://nlp.cs.illinois.edu/HockenmaierGroup/8k-pictures.html

  28. 28.

    http://web.engr.illinois.edu/~bplumme2/Flickr30kEntities/

  29. 29.

    http://cocodataset.org/

References

  • Artetxe M, Labaka G, Agirre E, Cho K (2017) Unsupervised neural machine translation. CoRR, abs/1710.11041

    Google Scholar 

  • Ba JL, Kiros R, Hinton EG (2016) Layer normalization. CoRR, abs/1607.06450

    Google Scholar 

  • Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. CoRR, abs/1409.0473

    Google Scholar 

  • Bernardi R, Çakici R, Elliott D, Erdem A, Erdem E, Ikizler-Cinbis N, Keller F, Muscat A, Plank B (2016) Automatic description generation from images: a survey of models, datasets, and evaluation measures. J Artif Intell Res 55:409–442

    Article  Google Scholar 

  • Bhirud SN, Bhavsar R, Pawar B (2017) Grammar checkers for natural languages:a review. Int J Natural Lang Comput 6(4):1

    Article  Google Scholar 

  • Brants T (2000) Tnt: a statistical part-of-speech tagger. In: ANLC’00, Stroudsburg. Association for Computational Linguistics, pp 224–231

    Google Scholar 

  • Brill E (1992) A simple rule-based part of speech tagger. In: ANLC, Stroudsburg, pp 152–155

    Google Scholar 

  • Britz D, Goldie A, Luong M, Le VQ (2017) Massive exploration of neural machine translation architectures. CoRR, abs/1703.03906

    Google Scholar 

  • Chieu LH, Ng TH (2002) Named entity recognition: a maximum entropy approach using global information. In: COLING, Taipei

    Book  Google Scholar 

  • Chiu JPC, Nichols E (2016) Named entity recognition with bidirectional LSTM-CNNs. TACL 4:357–370

    Google Scholar 

  • Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR, abs/1412.3555

    Google Scholar 

  • Chung J, Cho K, Bengio Y (2016) A character-level decoder without explicit segmentation for neural machine translation. In: ACL, Berlin

    Book  Google Scholar 

  • Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa PP (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12:2493–2537

    Google Scholar 

  • Costa-Jussà MR, Fonollosa JAR (2016) Character-based neural machine translation. In: ACL, Berlin

    Book  Google Scholar 

  • Dale R, Kilgarriff A (2011) Helping our own: the HOO 2011 pilot shared task. In: ENLG, Nancy, pp 242–249

    Google Scholar 

  • Daniel N (2003) A rule-based style and grammar checker. Master’s thesis, Bielefeld University, Bielefeld

    Google Scholar 

  • Daudaravicius V, Banchs ER, Volodina E, Napoles C (2016) A report on the automatic evaluation of scientific writing shared task. In: Proceedings of the 11th workshop on innovative use of NLP for building educational applications, BEA@NAACL-HLT 2016, San Diego, 16 June 2016, pp 53–62

    Google Scholar 

  • dos Santos CN, Gatti M (2014) Deep convolutional neural networks for sentiment analysis of short texts. In: COLING, Dublin, pp 69–78

    Google Scholar 

  • Elman LJ (1990) Finding structure in time. Cogn Sci 14(2):179–211

    Article  Google Scholar 

  • Firat O, Cho K, Sankaran B, Yarman-Vural FT, Bengio Y (2017) Multi-way, multilingual neural machine translation. Comput Speech Lang 45:236–252

    Article  Google Scholar 

  • Firth RJ (1957) A synopsis of linguistic theory 1930–1955. Studies in linguistic analysis. Blackwell, Oxford, pp 1–32

    Google Scholar 

  • Florian R, Ittycheriah A, Jing H, Zhang T (2003) Named entity recognition through classifier combination. In: Proceedings of the seventh conference on natural language learning, CoNLL 2003, Held in cooperation with HLT-NAACL 2003, Edmonton, 31 May–1 June 2003, pp 168–171

    Google Scholar 

  • Gehring J, Auli M, Grangier D, Dauphin Y (2017) A convolutional encoder model for neural machine translation. In: ACL, Vancouver, pp 123–135

    Google Scholar 

  • Gehring J, Auli M, Grangier D, Yarats D, Dauphin NY (2017) Convolutional sequence to sequence learning. In: ICML, Sydney, pp 1243–1252

    Google Scholar 

  • Gers AF, Schmidhuber J (2000) Recurrent nets that time and count. In: IJCNN (3), Como, pp 189–194

    Google Scholar 

  • Goodfellow JI, Bengio Y, Courville CA (2016) Deep learning. Adaptive computation and machine learning. MIT Press, Cambridge

    Google Scholar 

  • Graves A, Mohamed A, Hinton EG (2013) Speech recognition with deep recurrent neural networks. In: IEEE ICASSP, British Columbia, pp 6645–6649

    Google Scholar 

  • Greff K, Srivastava KR, Koutník J, Steunebrink RB, Schmidhuber J (2017) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222–2232

    Article  Google Scholar 

  • Gucehre C, Firat O, Xu K, Cho K, Barrault L, Lin H, Bougares F, Schwenk H, Bengio Y (2015) On using monolingual corpora in neural machine translation. CoRR, abs/1503.03535

    Google Scholar 

  • Harris Z (1954) Distributional structure. Word 10(23):146–162

    Article  Google Scholar 

  • He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR, Las Vegas, pp 770–778

    Google Scholar 

  • Hoang TD, Chollampatt S, Ng TH (2016) Exploiting n-best hypotheses to improve an SMT approach to grammatical error correction. In: IJCAI, pp 2803–2809

    Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  CAS  Google Scholar 

  • Hodosh M, Young P, Hockenmaier J (2013) Framing image description as a ranking task: data, models and evaluation metrics. J Artif Intell Res 47:853–899

    Article  Google Scholar 

  • Huang Z, Xu W, Yu K (2015) Bidirectional LSTM-CRF models for sequence tagging. CoRR, abs/1508.01991

    Google Scholar 

  • Hu Z, Zhang Z, Yang H, Chen Q, Zuo D (2017) A deep learning approach for predicting the quality of online health expert question-answering services. J Biomed Inform 71:241–253

    Article  Google Scholar 

  • Hu Z, Zhang Z, Yang H, Chen Q, Zhu R, Zuo D (2018) Predicting the quality of online health expert question-answering services with temporal features in a deep learning framework. Neurocomputing 275:2769–2782

    Article  Google Scholar 

  • Jean S, Cho K, Memisevic R, Bengio Y (2015) On using very large target vocabulary for neural machine translation. In: ACL, Beijing, pp 1–10

    Google Scholar 

  • Ji S, Vishwanathan SVN, Satish N, Anderson JM, Dubey P (2015) Blackout: speeding up recurrent neural network language models with very large vocabularies. CoRR, abs/1511.06909

    Google Scholar 

  • Johnson M, Schuster M, Le VQ, Krikun M, Wu Y, Chen Z, Thorat N, Viégas FB, Wattenberg M, Corrado G, Hughes M, Dean J (2017) Google’s multilingual neural machine translation system: enabling zero-shot translation. TACL 5:339–351

    Google Scholar 

  • Józefowicz R, Zaremba W, Sutskever I (2015) An empirical exploration of recurrent network architectures. In: ICML, Lille, pp 2342–2350

    Google Scholar 

  • Junczys-Dowmunt M, Grundkiewicz R (2016) Phrase-based machine translation is state-of-the-art for automatic grammatical error correction. In: EMNLP, Austin, pp 1546–1556

    Google Scholar 

  • Jurafsky D, Martin HJ (2017) Speech and language processing – an introduction to natural language processing. Computational linguistics, and speech recognition. 3rd edn. Prentice Hall, p 1032

    Google Scholar 

  • Karpathy A, Fei-Fei L (2017) Deep visual-semantic alignments for generating image descriptions. IEEE Trans Pattern Anal Mach Intell 39(4):664–676

    Article  Google Scholar 

  • Kim Y (2014) Convolutional neural networks for sentence classification. In: EMNLP, Doha, pp 1746–1751

    Google Scholar 

  • Kingma PD, Ba J (2014) Adam: a method for stochastic optimization. CoRR, abs/1412.6980

    Google Scholar 

  • Koehn P (2005) Europarl: a parallel corpus for statistical machine translation. In: MT summit, vol 5, pp 79–86

    Google Scholar 

  • Koutník J, Greff K, Gomez JF, Schmidhuber J (2014) A clockwork RNN. In: ICML, Beijing, pp 1863–1871

    Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton EG (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90

    Article  Google Scholar 

  • Lafferty DJ, McCallum A, Pereira FCN (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: ICML, Williams College, pp 282–289

    Google Scholar 

  • Lample G, Ballesteros M, Subramanian S, Kawakami K, Dyer C (2016) Neural architectures for named entity recognition. In: NAACL HLT, San Diego, pp 260–270

    Google Scholar 

  • Lample G, Denoyer L, Ranzato M (2017) Unsupervised machine translation using monolingual corpora only. CoRR, abs/1711.00043

    Google Scholar 

  • LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  • LeCun Y, Bengio Y, Hinton EG (2015) Deep learning. Nature 521(7553):436–444

    Article  CAS  Google Scholar 

  • Lewis DD, Yang Y, Rose GT, Li F (2004) RCV1: a new benchmark collection for text categorization research. J Mach Learn Res 5:361–397

    Google Scholar 

  • Lin T, Maire M, Belongie JS, Hays J, Perona P, Ramanan D, Dollár P, Zitnick LC (2014) Microsoft COCO: common objects in context. In: ECCV, Zurich, pp 740–755

    Google Scholar 

  • Luong M, Manning DC (2016) Achieving open vocabulary neural machine translation with hybrid word-character models. In: ACL, Berlin

    Book  Google Scholar 

  • Luong M, Le VQ, Sutskever I, Vinyals O, Kaiser L (2015a) Multi-task sequence to sequence learning. CoRR, abs/1511.06114

    Google Scholar 

  • Luong T, Pham H, Manning DC (2015b) Effective approaches to attention-based neural machine translation. In: EMNLP, Lisbon, pp 1412–1421

    Google Scholar 

  • Luong T, Sutskever I, Le VQ, Vinyals O, Zaremba W (2015c) Addressing the rare word problem in neural machine translation. In: ACL, Beijing, pp 11–19

    Google Scholar 

  • Ma X, Hovy HE (2016) End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: ACL, Berlin

    Book  Google Scholar 

  • Maas LA, Daly ER, Pham TP, Huang D, Ng YA, Potts C (2011) Learning word vectors for sentiment analysis. In: The 49th annual meeting of the Association for Computational Linguistics: human language technologies, proceedings of the conference, 19–24 June 2011, Portland, pp 142–150

    Google Scholar 

  • Manchanda B, Athavale AV, Kumar Sharma S (2016) Various techniques used for grammar checking. Int J Comput Appl Inf Technol 9(1):177

    Google Scholar 

  • Manning DC (2011) Part-of-speech tagging from 97% to 100%: is it time for some linguistics? In: CICLing, Tokyo, pp 171–189

    Google Scholar 

  • Marcus PM, Santorini B, Marcinkiewicz AM (1993) Building a large annotated corpus of English: the penn treebank. Comput Linguist 19(2):313–330

    Google Scholar 

  • Marcus M, Santorini B, Marcinkiewicz M, Taylor A (1999) Treebank-3 LDC99T42. Web Download. Linguistic Data Consortium, Philadelphia. https://catalog.ldc.upenn.edu/LDC99T42

    Google Scholar 

  • McCallum A, Freitag D, Pereira FCN (2000) Maximum entropy Markov models for information extraction and segmentation. In: ICML’00. Morgan Kaufmann Publishers Inc., San Francisco, pp 591–598

    Google Scholar 

  • Melamud O, McClosky D, Patwardhan S, Bansal M (2016) The role of context types and dimensionality in learning word embeddings. In: NAACL HLT, San Diego, pp 1030–1040

    Google Scholar 

  • Mi H, Wang Z, Ittycheriah A (2016) Vocabulary manipulation for neural machine translation. In: ACL, Berlin

    Book  Google Scholar 

  • Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. CoRR, abs/1301.3781

    Google Scholar 

  • Mikolov T, Sutskever I, Chen K, Corrado SG, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: NIPS, Lake Tahoe, pp 3111–3119

    Google Scholar 

  • Nazar R, Renau I (2012) Google books n-gram corpus used as a grammar checker. In: Proceedings of the second workshop on computational linguistics and writing (CLW 2012): linguistic and cognitive aspects of document creation and document engineering, EACL 2012, Stroudsburg. Association for Computational Linguistics, pp 27–34

    Google Scholar 

  • Ng TH, Wu MS, Wu Y, Hadiwinoto C, Tetreault RJ (2013) The conll-2013 shared task on grammatical error correction. In: Proceedings of the seventeenth conference on computational natural language learning: shared task, CoNLL 2013, Sofia, 8–9 Aug 2013, pp 1–12

    Google Scholar 

  • Ng TH, Wu MS, Briscoe T, Hadiwinoto C, Susanto HR, Bryant C (2014) The conll-2014 shared task on grammatical error correction. In: CoNLL, Baltimore, pp 1–14

    Google Scholar 

  • Nivre J et al (2017) Universal dependencies 2.1. LINDAT/CLARIN digital library at the Institute of Formal and Applied Linguistics ( ’UFAL), Faculty of Mathematics and Physics, Charles University

    Google Scholar 

  • Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting of the Association for Computational Linguistics, Barcelona, 21–26 July 2004, pp 271–278

    Google Scholar 

  • Pang B, Lee L (2005) Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: ACL 2005, 43rd annual meeting of the Association for Computational Linguistics, proceedings of the conference, 25–30 June 2005, University of Michigan, USA, pp 115–124

    Google Scholar 

  • Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. CoRR, cs.CL/0205070

    Google Scholar 

  • Pascanu R, Mikolov T, Bengio Y (2013) On the difficulty of training recurrent neural networks. In: ICML, Atlanta, pp 1310–1318

    Google Scholar 

  • Pennington J, Socher R, Manning DC (2014) Glove: global vectors for word representation. In: EMNLP, Doha, pp 1532–1543

    Google Scholar 

  • Plank B, Søgaard A, Goldberg Y (2016) Multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss. In: ACL, Berlin

    Book  Google Scholar 

  • Plummer AB, Wang L, Cervantes MC, Caicedo CJ, Hockenmaier J, Lazebnik S (2017) Flickr30k entities: collecting region-to-phrase correspondences for richer image-to-sentence models. Int J Comput Vis 123(1):74–93

    Article  Google Scholar 

  • Rong X (2014) word2vec parameter learning explained. CoRR, abs/1411.2738

    Google Scholar 

  • Rozovskaya A, Roth D (2010) Training paradigms for correcting errors in grammar and usage. In: HLT’10, Stroudsburg. Association for Computational Linguistics, pp 154–162

    Google Scholar 

  • Rozovskaya A, Roth D (2016) Grammatical error correction: machine translation and classifiers. In: ACL, Berlin

    Google Scholar 

  • Ruder S (2016) An overview of gradient descent optimization algorithms. CoRR, abs/1609.04747

    Google Scholar 

  • Ruder S, Ghaffari P, Breslin GJ (2016) A hierarchical model of reviews for aspect-based sentiment analysis. In: EMNLP, Austin, pp 999–1005

    Google Scholar 

  • Schmaltz A, Kim Y, Rush MA, Shieber MS (2016) Sentence-level grammatical error identification as sequence-to-sequence correction. In: Proceedings of the 11th workshop on innovative use of NLP for building educational applications, BEA@NAACL-HLT 2016, 16 June 2016, San Diego, pp 242–251

    Google Scholar 

  • Sennrich R, Haddow B, Birch A (2016a) Improving neural machine translation models with monolingual data. In: ACL, Berlin

    Book  Google Scholar 

  • Sennrich R, Haddow B, Birch A (2016b) Neural machine translation of rare words with subword units. In: ACL, Berlin

    Book  Google Scholar 

  • Srivastava N, Hinton EG, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    Google Scholar 

  • Sutskever I, Vinyals O, Le VQ (2014) Sequence to sequence learning with neural networks. In: NIPS, Montreal, pp 3104–3112

    Google Scholar 

  • Toutanova K, Klein D, Manning DC, Singer Y (2003) Feature-rich part-of-speech tagging with a cyclic dependency network. In: HLT-NAACL, Edmonton

    Book  Google Scholar 

  • Ueffing N, Ney H (2003) Using POS information for statistical machine translation into morphologically rich languages. In: EACL’03, Stroudsburg. Association for Computational Linguistics, pp 347–354

    Google Scholar 

  • Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez NA, Kaiser L, Polosukhin I (2017) Attention is all you need. In: NIPS, Long Beach, pp 6000–6010

    Google Scholar 

  • Vinyals O, Toshev A, Bengio S, Erhan D (2017) Show and tell: lessons learned from the 2015 MSCOCO image captioning challenge. IEEE Trans Pattern Anal Mach Intell 39(4):652–663

    Article  Google Scholar 

  • Wang P, Qian Y, Soong KF, He L, Zhao H (2015) A unified tagging solution: bidirectional LSTM recurrent neural network with word embedding. CoRR, abs/1511.00215

    Google Scholar 

  • Wiseman S, Rush MA (2016) Sequence-to-sequence learning as beam-search optimization. In: EMNLP, Austin, pp 1296–1306

    Google Scholar 

  • Wu J, Chang J, Chang SJ (2013) Correcting serial grammatical errors based on n-grams and syntax. IJCLCLP 18(4)

    Google Scholar 

  • Wu Y, Schuster M, Chen Z, Le VQ, Norouzi M, Macherey W, Krikun M, Cao Y, Gao Q, Macherey K, Klingner J, Shah A, Johnson M, Liu X, Kaiser L, Gouws S, Kato Y, Kudo T, Kazawa H, Stevens K, Kurian G, Patil N, Wang W, Young C, Smith J, Riesa J, Rudnick A, Vinyals O, Corrado G, Hughes M, Dean J (2016) Google’s neural machine translation system: bridging the gap between human and machine translation. CoRR, abs/1609.08144

    Google Scholar 

  • Xu K, Ba J, Kiros R, Cho K, Courville CA, Salakhutdinov R, Zemel SR, Bengio Y (2015) Show, attend and tell: Neural image caption generation with visual attention. In: ICML, Lille, pp 2048–2057

    Google Scholar 

  • Yang Z, Salakhutdinov R, Cohen WW (2016) Multi-task cross-lingual sequence tagging from scratch. CoRR, abs/1603.06270

    Google Scholar 

  • Yin W, Yu M, Xiang B, Zhou B, Schütze H (2016) Simple question answering by attentive convolutional neural network. In: COLING, Osaka, pp 1746–1756

    Google Scholar 

  • Zhou J, Cao Y, Wang X, Li P, Xu W (2016) Deep recurrent models with fast-forward connections for neural machine translation. TACL 4:371–383

    Google Scholar 

  • Ziemski M, Junczys-Dowmunt M, Pouliquen B (2016) The united nations parallel corpus v1.0. In: Proceedings of the tenth international conference on language resources and evaluation LREC 2016, Portorovz, 23–28 May 2016

    Google Scholar 

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Acknowledgements

The work described in this paper was partially supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. UGC/IDS14/16).

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  1. Department of Computing, Deep Learning Research and Application Centre, Hang Seng Management College, Sha Tin, Hong Kong

    Haiqin Yang, Linkai Luo, Lap Pong Chueng, David Ling & Francis Chin

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  1. Haiqin Yang
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  1. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Kaizhu Huang

  2. School of Computing, Edinburgh Napier University, Edinburgh, UK

    Amir Hussain

  3. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Qiu-Feng Wang

  4. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Rui Zhang

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Yang, H., Luo, L., Chueng, L.P., Ling, D., Chin, F. (2019). Deep Learning and Its Applications to Natural Language Processing. In: Huang, K., Hussain, A., Wang, QF., Zhang, R. (eds) Deep Learning: Fundamentals, Theory and Applications. Cognitive Computation Trends, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-030-06073-2_4

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