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NLP Review: Architectures, Techniques, Applications and Challenges

by Ankit Sirmorya, Sowmyashree Ramesh Kumar, Mehul Vishal Sadh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 8
Year of Publication: 2022
Authors: Ankit Sirmorya, Sowmyashree Ramesh Kumar, Mehul Vishal Sadh
10.5120/ijca2022922049

Ankit Sirmorya, Sowmyashree Ramesh Kumar, Mehul Vishal Sadh . NLP Review: Architectures, Techniques, Applications and Challenges. International Journal of Computer Applications. 184, 8 ( Apr 2022), 1-8. DOI=10.5120/ijca2022922049

@article{ 10.5120/ijca2022922049,
author = { Ankit Sirmorya, Sowmyashree Ramesh Kumar, Mehul Vishal Sadh },
title = { NLP Review: Architectures, Techniques, Applications and Challenges },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2022 },
volume = { 184 },
number = { 8 },
month = { Apr },
year = { 2022 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number8/32346-2022922049/ },
doi = { 10.5120/ijca2022922049 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:20:55.466914+05:30
%A Ankit Sirmorya
%A Sowmyashree Ramesh Kumar
%A Mehul Vishal Sadh
%T NLP Review: Architectures, Techniques, Applications and Challenges
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 8
%P 1-8
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The natural language processing (NLP) field entails the application of a broad range of computational approaches to the automatic analysis and representation of human language. It’s a field of artificial intelligence in which computers analyze, understand, and derive meaning information from human language in a smart and useful way. A large percentage of NLP applications are used to organize and structure knowledge in order to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation. The paper goes through different NLP architectures that can perform such tasks. Various architectures have been discussed in detail, such as CNN, RNN, LSTM, and GRU. Additionally, we cover NLP Techniques such as Morphological Analysis, Semantic Analysis, Sentiment Analysis, Keyword Extraction, Stemming, and Lemmatization. There are also several limitations of this methodology. Almost every industry uses NLP. NLP plays a major role in many fields like Health care, Information Retrieval, and Web mining. We finally gave a brief review on different NLP topics and future research.

References
  1. James F. Allen. 2003. Natural language processing. Encyclopedia of Computer Science. John Wiley and Sons Ltd., GBR, 1218–1222.
  2. Elizabeth D. Liddy: Natural Language Processing
  3. Jones, K. S. (1994). Natural Language Processing: A Historical Review. Current Issues in Computational Linguistics: In Honour of Don Walker, 3–16.
  4. Hutchins, J. (2005). ”The history of machine translation in a nutshell” (PDF).
  5. Wikipedia, “Natural Language Processing”, NLP.
  6. Spyns, Peter. ”Natural language processing in medicine: an overview.” Methods of information in medicine 35.04/05 (1996): 285-301.
  7. McCray AT, Sponsler J, Brylawski B, Browne A. The Role of Lexical Knowledge in Biomedical Text Understanding. In: SCAMC 87. IEEE Computer Society Press, 1987: 103-7.
  8. McCray AT. Natural language processing for intelligent information retrieval. In: Nagel J, Smith W, eds.IEEE 1991. Orlando, 1991: 1160-1.
  9. McCray AT. Extending a Natural Language Parser with UMLS knowledge. In: Clayton P, ed. SCAMC 91. McGraw Hill, 1991: 194-8.
  10. McCray AT, Srinivasan S, Browne A. Lexical Methods for Managing Variation in Biomedical Terminologies. In: SCAMC 94. 1994: 235-9.
  11. McCray AT, Razi A. The UMLS Knowledge Source Server. In: Greenes R, Peterson H, Protti D, eds. MEDINFO 95. Edmonton: Healthcare Computing Communications, 1995: 144- 7.
  12. McCray AT, Nelson S. The Representation of Meaning in the UMLS. Meth Inform Med 1995; 34: 193-201.
  13. Lyman M, Sager N, Friedman C, Chi E. Computer-structured Narrative in Ambulatory Care: Its Use in Longitudinal Review of Clinical Data. In: SCAMC 85. IEEE, 1985: 82-6
  14. Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Commun ACM 18(11):613. doi:10.1145/361219.361220
  15. Weizenbaum J (1976) Computer power and human reason: from judgment to calculation. W H Freeman and Company, San Francisco, ISBN 0-7167-0463-3
  16. Quarteroni, Silvia. ”Natural language processing for industry.” Informatik-Spektrum 41.2 (2018): 105-112.
  17. Jusoh, Shaidah. ”A STUDY ON NLP APPLICATIONS AND AMBIGUITY PROBLEMS.” Journal of Theoretical Applied Information Technology 96.6 (2018).
  18. Hirschman, L., Gaizauskas, R. (2001). Natural language question answering: the view from here. Natural Language Engineering, 7, 275–300.
  19. Alfawareh, H.M. Jusoh, S. (2011). Resolving ambiguous entity through context knowledge and fuzzy approach. International Journal on Computer Science and Engineering (IJCSE), 3 (1), 410 – 422.
  20. In´es Rold´os: “Major Challenges of Natural Language Processing (NLP)”, monkeylearn.com/blog/natural-languageprocessing- challenges
  21. Reshamwala, Alpa Mishra, Dhirendra Pawar, Prajakta. (2013). REVIEW ON NATURAL LANGUAGE PROCESSING. IRACST – Engineering Science and Technology: An International Journal (ESTIJ). 3. 113-116.
  22. “Language Processing - Semantic Analysis”, semanticanalysis
  23. Natural Language Processing - Syntactic Analysis, syntaticanalysis (24) Roxana Girju, “Introduction to Syntactic Parsing”, 2004
  24. Mallamma V. Redd, Hanumanthappa M.,“Semantical and Syntactical Analysis of NLP ”
  25. Chowdhary, KR1442. ”Natural language processing.” Fundamentals of artificial intelligence (2020): 603-649.
  26. Hirschberg, Julia, and Christopher D.Manning. ”Advances in natural language processing.” Science 349.6245 (2015): 261- 266.
  27. Akash Shastri, “3 neural network architectures you need to know for NLP!”, https://towardsdatascience.com/3- neural-network-architectures-you-need-to-know-for-nlp- 5660f11281be
  28. R. Collobert and J. Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning,” in Proc. 25th Int. Conf. Machine Learning, 2008, pp. 160–167
  29. Yin, Wenpeng, et al. ”Comparative study of CNN and RNN for natural language processing.” arXiv preprint arXiv:1702.01923 (2017).
  30. LeCun, Yann, et al. ”Gradient-based learning applied to document recognition.” Proceedings of the IEEE 86.11 (1998): 2278-2324.
  31. Elman, Jeffrey L. ”Finding structure in time.” Cognitive science 14.2 (1990): 179-211.
  32. Hochreiter, Sepp, and J¨urgen Schmidhuber. ”Long short-term memory.” Neural computation 9.8 (1997): 1735-1780.
  33. J. Shin, Y. Kim, S. Yoon and K. Jung, ”Contextual-CNN: A Novel Architecture Capturing Unified Meaning for Sentence Classification,” 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), 2018, pp. 491-494, doi: 10.1109/BigComp.2018.00079.
  34. Christopher Thomas: “ Recurrent Neural Networks and Natural Language Processing.” , https://towardsdatascience.com/recurrent-neural-networksand- natural-language-processing-73af640c2aa1
  35. Batur Dinler O¨ , Aydin N. An optimal feature parameter set based on gated recurrent unit recurrent neural net- works for speech segment detection. Appl Sci. 2020;10(4):1273.
  36. Jagannatha AN, Yu H. Structured prediction models for RNN based sequence labeling in clinical text. In: Proceed- ings of the conference on empirical methods in natural language processing. conference on empirical methods in natural language processing, vol. 2016, NIH Public Access; 2016. p. 856.
  37. Wikipedia, “Long short term memory”, LSTM
  38. Smeaton, Alan F. ”Using NLP or NLP resources for information retrieval tasks.” Natural language information retrieval. Springer, Dordrecht, 1999. 99-111.
  39. Brants, Thorsten. ”Natural Language Processing in Information Retrieval.” CLIN 111 (2003).
  40. Smeaton, Alan F. ”Progress in the application of natural language processing to information retrieval tasks.” The computer journal 35.3 (1992): 268-278.
  41. Kosala, Raymond, and Hendrik Blockeel. ”Web mining research: A survey.” ACM Sigkdd Explorations Newsletter 2.1 (2000): 1-15.
  42. Van Rijsbergen. Information Retrieval. Butterworths, 1979.
  43. J. Cowie and W. Lehnert. Information extraction. Communications of the ACM, 39(1):80-91, 1996.
  44. Kushmerick. Gleaning the web. IBBE Intelligent Systems, 14(2):20-22, 1999.
  45. I. Muslea, S. Minton, and C. Knoblock. Wrapper induction for semistructured, web-based information sources. In Proceedings of the Conference on Automatic Learning and Discovery CONALD-98, 1999
  46. Marzban, Reza, and Christopher Crick. ”Lifting Sequence Length Limitations of NLP Models using Autoencoders.” ICPRAM. 2021.
  47. Alayba, Abdulaziz M., et al. ”A combined CNN and LSTM model for arabic sentiment analysis.” International crossdomain conference for machine learning and knowledge extraction. Springer, Cham, 2018.
  48. Cho, Kyunghyun, et al. ”Learning phrase representations using RNN encoder-decoder for statistical machine translation.” arXiv preprint arXiv:1406.1078 (2014).
  49. Yang, Shudong, Xueying Yu, and Ying Zhou. ”Lstm and gru neural network performance comparison study: Taking yelp review dataset as an example.” 2020 International workshop on electronic communication and artificial intelligence (IWECAI). IEEE, 2020.
  50. Yoshua Bengio, Patrice Simard, and Paolo Frasconi. Learning long-term dependencies with gradient descent is difficult. Neural Networks, 5(2):157–166, 1994.
  51. Christopher Thomas: “Recurrent Neural Networks and Natural Language Processing”, RNN
  52. Vibhor nigam”: “Natural Language Processing: From Basics to using RNN and LSTM”,https://medium.com/analyticsvidhya/ natural-language-processing-from-basics-to-usingrnn- and-lstm-ef6779e4ae66
  53. W. Wang and J. Gang, ”Application of Convolutional Neural Network in Natural Language Processing,” 2018 International Conference on Information Systems and Computer Aided Education (ICISCAE), 2018, pp. 64-70, doi: 10.1109/ICISCAE. 2018.8666928.
  54. Y. Kim, ”Convolutional neural networks for sentence classification.” ArXiv preprint arXiv: 1408.5882, 2014
  55. Santhosh Kumar T: “Natural Language Processing – Sentiment Analysis using LSTM, https://www.analyticsvidhya.com/blog/2021/06/naturallanguage- processing-sentiment-analysis-using-lstm/
  56. Huang, Zhiheng, Wei Xu, and Kai Yu. ”Bidirectional LSTM-CRF models for sequence tagging.” arXiv preprint arXiv:1508.01991 (2015).
  57. Makarenkov, Victor Rokach, Lior Shapira, Bracha. (2019). Choosing the Right Word: Using Bidirectional LSTM Tagger for Writing Support Systems.
  58. Mathworks,“Multilabel Text Classification Using Deep Learning”, mathworks.com/help/deeplearning/ug/multilabeltext- classification-using-deep-learning.html
  59. Liu, Li, et al. ”Global, regional, and national causes of child mortality in 2000–13, with projections to inform post-2015 priorities: an updated systematic analysis.” The Lancet 385.9966 (2015): 430-440.
  60. Prabowo, Rudy, and Mike Thelwall. ”Sentiment analysis: A combined approach.” Journal of Informetrics 3.2 (2009): 143- 157.
  61. Feldman, Ronen. ”Techniques and applications for sentiment analysis.” Communications of the ACM 56.4 (2013): 82-89.
  62. Balakrishnan, Vimala, and Ethel Lloyd-Yemoh. ”Stemming and lemmatization: a comparison of retrieval performances.” (2014): 174-179.
  63. Korenius, Tuomo, et al. ”Stemming and lemmatization in the clustering of finnish text documents.” Proceedings of the thirteenth ACM international conference on Information and knowledge management. 2004.
  64. Hull, D. Stemming algorithms: a case study for detailed evaluation. Journal of the American Society for Information Science, 47, 1 (1996), 70-84
  65. Alkula, R. From plain character strings to meaningful words: Producing better full text databases for inflectional and compounding languages with morphological analysis software. Information Retrieval, 4, (2001), 195-208.
  66. Rose, Stuart, et al. ”Automatic keyword extraction from individual documents.” Text mining: applications and theory 1 (2010): 1-20.
  67. Turney, Peter. (2000). Learning Algorithms for Keyphrase Extraction. Inf. Retr.. 2. 303-336. 10.1023/A:1009976227802.
  68. Hulth, Anette. ”Improved automatic keyword extraction given more linguistic knowledge.” Proceedings of the 2003 conference on Empirical methods in natural language processing. 2003.
  69. Rahul Agarwal: ”NLP Learning Series: Part 3 - Attention, CNN and what not for Text Classification”, RNN (71) ”Understanding LSTM Networks” LSTM
Index Terms

Computer Science
Information Sciences

Keywords

Deep Learning Machine Learning

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