Review
BibTex RIS Cite

Natural Language Processing Challenges and Issues: A Literature Review

Year 2023, Volume: 36 Issue: 4, 1522 - 1536, 01.12.2023
https://doi.org/10.35378/gujs.1032517

Abstract

Natural Language Processing (NLP) is the computerized approach to analyzing text using both structured and unstructured data. NLP is a simple, empirically powerful, and reliable approach. It achieves state-of-the-art performance in language processing tasks like Semantic Search (SS), Machine Translation (MT), Text Summarization (TS), Sentiment Analyzer (SA), Named Entity Recognition (NER) and Emotion Detection (ED). NLP is expected to be the technology of the future, based on current technology deployment and adoption. The primary question is: What does NLP have to offer in terms of reality, and what are the prospects? There are several problems to be addressed with this developing method, as it must be compatible with future technology. In this paper, the benefits, challenges and limitations of this innovative paradigm along with the areas open to do research are shown.

References

  • [1] Bahdanau, D., Cho, K., Bengio, Y., "Neural machine translation by jointly learning to align and translate", International Conference on Learning Representations (ICLR), Conference, USA, 1- 15, (2015).
  • [2] Luong, M.T., Pham, H., Manning, C.D., "Effective approaches to attention-based neural machine translation", Empirical Methods in Natural Language Processing (EMNLP), Conference, Portugal, 1412-1421, (2015).
  • [3] Yadav, V., Bethard, S., "A survey on recent advances in named entity recognition from deep learning models", Proceedings of the 27th International Computational Linguistics, Conference, USA, 2145–2158, (2019).
  • [4] Singh, S., Mahmood, A., "The NLP Cookbook: Modern Recipes for Transformer Based Deep Learning Architectures", IEEE Access, 4: 68675-68702, (2021).
  • [5] Kang, Y., Cai, Z., Tan, C. W., Huang, Q., Liu H., "Natural language processing (NLP) in management research: A literature review", Journal of Management Analytics, 7(2): 139-172 (2020).
  • [6] Nemeshaev, S., Barykin, L., Dadteev, K., "Selection of experts for scientific and technical expertise based on semantic search", Procedia Computer Science, 190: 643-646, (2021).
  • [7] Ott, M., Edunov, S., Grangier, D., Auli, M., "Scaling neural machine translation", Proceedings of the Machine Translation, Conference, USA, 1: 1-9, (2018).
  • [8] Wang, H., Wu, H., He, Z., Huang, L., Church, K.W., "Progress in Machine Translation", Engineering, 3: 1-19, (2021).
  • [9] Alomari, A., Idris, N., Sabri, A. Q. Alsmadi, I., "Deep Reinforcement and Transfer Learning for Abstractive Text Summarization: A Review", Computer Speech & Language, 71: 1-43, (2021).
  • [10] Merchant, K., Pande, Y., "Nlp based latent semantic analysis for legal text summarization", International Conference on Advances in Computing, Communications and Informatics (ICACCI), India, 1803-1807, (2018).
  • [11] Zhang, Y., Tuo, M., Yin, Q., Qi, L., Wang, X., Liu, T., "Keywords extraction with deep neural network model", Neurocomputing, 383: 113-121, (2020).
  • [12] Firoozeh, N., Nazarenko, A., Alizon, F., Daille, B., "Keyword extraction: Issues and methods", Natural Language Engineering, 26(3): 259-291, (2020).
  • [13] Yi, J., Nasukawa, T., Bunescu, R., Niblack, W., "Sentiment analyzer: “Extracting sentiments about a given topic using natural language processing techniques", IEEE Data Mining, Conference, USA, 1-8, (2003).
  • [14] Ritter, A., Clark, S., Etzioni, O., "Named entity recognition in tweets: an experimental study", Proceedings of Empirical Methods in Natural Language Processing, Conference, UK, 1524-1534, (2011).
  • [15] Sharma, S., Srinivas, PY., Balabantaray, R., "Emotion detection using online machine learning method and TLBO on mixed script", In Language Resources and Evaluation Workshop (LREC), 47-51, Slovenia, (2016).
  • [16] Seal, D., Roy, UK., Basak, R., "Sentence-level emotion detection from text based on semantic rules", In Information and Communication Technology for Sustainable Development, 933: 423-430, Springer, Singapore, (2019).
  • [17] Chrupała, G., "Text segmentation with character-level text embeddings", Workshop on Deep Learning for Audio, Speech and Language Processing (ICML), 1-5, USA, (2013).
  • [18] Nguyen, H., Calantone, R., Krishnan, R., "Influence of social media emotional word of mouth on institutional investors decisions and firm value", Management Science, 66(2): 503-529, (2019).
  • [19] Dyer, T., Lang, M., Stice-Lawrence, L., "The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation", Journal of Accounting and Economics, 64(2-3): 221-245, (2017).
  • [20] Calomiris, C. W., Mamaysky, H., "How news and its context drive risk and returns around the world", Journal of Financial Economics, 133(2): 299-336, (2019).
  • [21] Froot, K., Kang, N., Ozik, G., Sadka, R., "What do measures of real-time corporate sales say about earnings surprises and post-announcement returns", Journal of Financial Economics, 125(1): 143-162, (2017).
  • [22] Wu, L., Lou, B., Hitt, L., "Data analytics supports decentralized innovation", Management Science, 65(10): 4451-4469, (2018).
  • [23] Ghose, A., Ipeirotis, P.G., Li, B., "Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content", Marketing Science, 31(3): 369-371, (2012).
  • [24] Ramaswamy, S., DeClerck, N., "Customer perception analysis using deep learning and NLP", Procedia Computer Science, 140: 170-178, (2018).
  • [25] Barlow, M. A., Verhaal, J. C., Angus, R. W., "Optimal distinctiveness, strategic categorization, and product market entry on the Google Play app platform", Strategic Management Journal, 40(8): 1219-1242, (2019).
  • [26] Zhou, Y., Zheng, X., Hsieh, C. J., Chang, K. W., Huang, X., "Defense against adversarial attacks in nlp via dirichlet neighborhood ensemble" Association for Computational Linguistics (ACL), Conference, China, 2(3): 1-12, (2020).
  • [27] Dalton, A., Aghaei, E., Al-Shaer, E., Bhatia, A., Castillo, E., Cheng, Z., Dhaduvai, S., Duan, Q., Hebenstreit, B., Islam, M. M., Karimi, Y., "Proceedings of the Workshop on Social Threats in Online Conversations: Understanding and Management, (STOC)", 1-8, France, (2020).
  • [28] Ray, A., Bala, P. K., Kumar, R., "An NLP-SEM approach to examine the gratifications affecting user’s choice of different e-learning providers from user tweets", Journal of Decision Systems, 30(4): 439-445, (2020).
  • [29] Pan. P., Chen, Y., "Automatic subject classification of public messages in e-government affairs", Data and Information Management, 5(3): 336-347, (2021).
  • [30] Tvardik, N., Kergourlay, I., Bittar, A., Segond, F., Darmoni, S., Metzger, M. H., "Accuracy of using natural language processing methods for identifying healthcare-associated infections", International Journal of Medical Informatics, 117: 96-102, (2018).
  • [31] Jain, K., Prajapati, V., "NLP/Deep Learning Techniques in Healthcare for Decision Making", Primary Health Care: Open Access, 11(3): 373-380, (2021).
  • [32] Carchiolo, V., Longheu, A., Reitano, G., Zagarella, L., "Medical prescription classification: a NLP-based approach", Federated Conference on Computer Science and Information Systems (FedCSIS), Germany, 605-609, (2019).
  • [33] Yu J, Luo G, Xiao T, Zhong Q, Wang Y, Feng W, Luo J, Wang C, Hou L, Li J, Liu Z. “MOOCCube: a large-scale data repository for NLP applications in MOOCs”, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 3135-3142, (2020).
  • [34] Alblawi, AS., Alhamed, A. A., "Big data and learning analytics in higher education: Demystifying variety, acquisition, storage, NLP and analytics", IEEE Conference on Big Data and Analytics (ICBDA), Malaysia, 124-129, (2017).
  • [35] Lalwani, T., Bhalotia, S., Pal, A., Rathod, V., Bisen, S., "Implementation of a Chatbot System using AI and NLP", International Journal of Innovative Research in Computer Science & Technology (IJIRCST), 6(3): 26-30, (2018).
  • [36] Häse, F., Roch, L. M., Aspuru-Guzik, A., "Next-generation experimentation with self-driving laboratories", Trends in Chemistry, 1(3): 282-291, (2019).
  • [37] Chandio, J. A., Talpur, M. S. H., Abro, A. A., Bux, H., Khokhar, N. U. A. A., Shah, A. A., Saima, M., “Study Of Customers Perception About Shopping Trend Involving E-Commerce: A Comparative Study”, Turkish Online Journal of Qualitative Inquiry, 12(8): 5415-5424, (2021).
  • [38] Abro, A. A., Khan, A. A., Talpur, M. S. H., Kayijuka, I., “Machine Learning Classifiers: A Brief Primer”, University of Sindh Journal of Information and Communication Technology, 5(2): 63-68, (2021)
  • [39] Abro, A. A., Taşci, E., Aybars, U. G., "A Stacking-based Ensemble Learning Method for Outlier Detection", Balkan Journal of Electrical and Computer Engineering, 8(2): 181-185, (2020).
  • [40] Abro, A. A., Yimer, M. A., Bhatti, Z., "Identifying the Machine Learning Techniques for Classification of Target Datasets", Sukkur IBA Journal of Computing and Mathematical Sciences, 4(1): 45-52, (2020).
  • [41] Abro, A. A., "Vote-Based: Ensemble Approach", Sakarya University Journal of Science, 25(3): 858-866, (2021).
  • [42] Abro, A. A., Siddique, W. A., Talpur, M. S. H., Jumani, A. K., Yaşar, E., “A combined approach of base and meta learners for hybrid system”, Turkish Journal of Engineering, 7(1): 25-32, (2023).
Year 2023, Volume: 36 Issue: 4, 1522 - 1536, 01.12.2023
https://doi.org/10.35378/gujs.1032517

Abstract

References

  • [1] Bahdanau, D., Cho, K., Bengio, Y., "Neural machine translation by jointly learning to align and translate", International Conference on Learning Representations (ICLR), Conference, USA, 1- 15, (2015).
  • [2] Luong, M.T., Pham, H., Manning, C.D., "Effective approaches to attention-based neural machine translation", Empirical Methods in Natural Language Processing (EMNLP), Conference, Portugal, 1412-1421, (2015).
  • [3] Yadav, V., Bethard, S., "A survey on recent advances in named entity recognition from deep learning models", Proceedings of the 27th International Computational Linguistics, Conference, USA, 2145–2158, (2019).
  • [4] Singh, S., Mahmood, A., "The NLP Cookbook: Modern Recipes for Transformer Based Deep Learning Architectures", IEEE Access, 4: 68675-68702, (2021).
  • [5] Kang, Y., Cai, Z., Tan, C. W., Huang, Q., Liu H., "Natural language processing (NLP) in management research: A literature review", Journal of Management Analytics, 7(2): 139-172 (2020).
  • [6] Nemeshaev, S., Barykin, L., Dadteev, K., "Selection of experts for scientific and technical expertise based on semantic search", Procedia Computer Science, 190: 643-646, (2021).
  • [7] Ott, M., Edunov, S., Grangier, D., Auli, M., "Scaling neural machine translation", Proceedings of the Machine Translation, Conference, USA, 1: 1-9, (2018).
  • [8] Wang, H., Wu, H., He, Z., Huang, L., Church, K.W., "Progress in Machine Translation", Engineering, 3: 1-19, (2021).
  • [9] Alomari, A., Idris, N., Sabri, A. Q. Alsmadi, I., "Deep Reinforcement and Transfer Learning for Abstractive Text Summarization: A Review", Computer Speech & Language, 71: 1-43, (2021).
  • [10] Merchant, K., Pande, Y., "Nlp based latent semantic analysis for legal text summarization", International Conference on Advances in Computing, Communications and Informatics (ICACCI), India, 1803-1807, (2018).
  • [11] Zhang, Y., Tuo, M., Yin, Q., Qi, L., Wang, X., Liu, T., "Keywords extraction with deep neural network model", Neurocomputing, 383: 113-121, (2020).
  • [12] Firoozeh, N., Nazarenko, A., Alizon, F., Daille, B., "Keyword extraction: Issues and methods", Natural Language Engineering, 26(3): 259-291, (2020).
  • [13] Yi, J., Nasukawa, T., Bunescu, R., Niblack, W., "Sentiment analyzer: “Extracting sentiments about a given topic using natural language processing techniques", IEEE Data Mining, Conference, USA, 1-8, (2003).
  • [14] Ritter, A., Clark, S., Etzioni, O., "Named entity recognition in tweets: an experimental study", Proceedings of Empirical Methods in Natural Language Processing, Conference, UK, 1524-1534, (2011).
  • [15] Sharma, S., Srinivas, PY., Balabantaray, R., "Emotion detection using online machine learning method and TLBO on mixed script", In Language Resources and Evaluation Workshop (LREC), 47-51, Slovenia, (2016).
  • [16] Seal, D., Roy, UK., Basak, R., "Sentence-level emotion detection from text based on semantic rules", In Information and Communication Technology for Sustainable Development, 933: 423-430, Springer, Singapore, (2019).
  • [17] Chrupała, G., "Text segmentation with character-level text embeddings", Workshop on Deep Learning for Audio, Speech and Language Processing (ICML), 1-5, USA, (2013).
  • [18] Nguyen, H., Calantone, R., Krishnan, R., "Influence of social media emotional word of mouth on institutional investors decisions and firm value", Management Science, 66(2): 503-529, (2019).
  • [19] Dyer, T., Lang, M., Stice-Lawrence, L., "The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation", Journal of Accounting and Economics, 64(2-3): 221-245, (2017).
  • [20] Calomiris, C. W., Mamaysky, H., "How news and its context drive risk and returns around the world", Journal of Financial Economics, 133(2): 299-336, (2019).
  • [21] Froot, K., Kang, N., Ozik, G., Sadka, R., "What do measures of real-time corporate sales say about earnings surprises and post-announcement returns", Journal of Financial Economics, 125(1): 143-162, (2017).
  • [22] Wu, L., Lou, B., Hitt, L., "Data analytics supports decentralized innovation", Management Science, 65(10): 4451-4469, (2018).
  • [23] Ghose, A., Ipeirotis, P.G., Li, B., "Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content", Marketing Science, 31(3): 369-371, (2012).
  • [24] Ramaswamy, S., DeClerck, N., "Customer perception analysis using deep learning and NLP", Procedia Computer Science, 140: 170-178, (2018).
  • [25] Barlow, M. A., Verhaal, J. C., Angus, R. W., "Optimal distinctiveness, strategic categorization, and product market entry on the Google Play app platform", Strategic Management Journal, 40(8): 1219-1242, (2019).
  • [26] Zhou, Y., Zheng, X., Hsieh, C. J., Chang, K. W., Huang, X., "Defense against adversarial attacks in nlp via dirichlet neighborhood ensemble" Association for Computational Linguistics (ACL), Conference, China, 2(3): 1-12, (2020).
  • [27] Dalton, A., Aghaei, E., Al-Shaer, E., Bhatia, A., Castillo, E., Cheng, Z., Dhaduvai, S., Duan, Q., Hebenstreit, B., Islam, M. M., Karimi, Y., "Proceedings of the Workshop on Social Threats in Online Conversations: Understanding and Management, (STOC)", 1-8, France, (2020).
  • [28] Ray, A., Bala, P. K., Kumar, R., "An NLP-SEM approach to examine the gratifications affecting user’s choice of different e-learning providers from user tweets", Journal of Decision Systems, 30(4): 439-445, (2020).
  • [29] Pan. P., Chen, Y., "Automatic subject classification of public messages in e-government affairs", Data and Information Management, 5(3): 336-347, (2021).
  • [30] Tvardik, N., Kergourlay, I., Bittar, A., Segond, F., Darmoni, S., Metzger, M. H., "Accuracy of using natural language processing methods for identifying healthcare-associated infections", International Journal of Medical Informatics, 117: 96-102, (2018).
  • [31] Jain, K., Prajapati, V., "NLP/Deep Learning Techniques in Healthcare for Decision Making", Primary Health Care: Open Access, 11(3): 373-380, (2021).
  • [32] Carchiolo, V., Longheu, A., Reitano, G., Zagarella, L., "Medical prescription classification: a NLP-based approach", Federated Conference on Computer Science and Information Systems (FedCSIS), Germany, 605-609, (2019).
  • [33] Yu J, Luo G, Xiao T, Zhong Q, Wang Y, Feng W, Luo J, Wang C, Hou L, Li J, Liu Z. “MOOCCube: a large-scale data repository for NLP applications in MOOCs”, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 3135-3142, (2020).
  • [34] Alblawi, AS., Alhamed, A. A., "Big data and learning analytics in higher education: Demystifying variety, acquisition, storage, NLP and analytics", IEEE Conference on Big Data and Analytics (ICBDA), Malaysia, 124-129, (2017).
  • [35] Lalwani, T., Bhalotia, S., Pal, A., Rathod, V., Bisen, S., "Implementation of a Chatbot System using AI and NLP", International Journal of Innovative Research in Computer Science & Technology (IJIRCST), 6(3): 26-30, (2018).
  • [36] Häse, F., Roch, L. M., Aspuru-Guzik, A., "Next-generation experimentation with self-driving laboratories", Trends in Chemistry, 1(3): 282-291, (2019).
  • [37] Chandio, J. A., Talpur, M. S. H., Abro, A. A., Bux, H., Khokhar, N. U. A. A., Shah, A. A., Saima, M., “Study Of Customers Perception About Shopping Trend Involving E-Commerce: A Comparative Study”, Turkish Online Journal of Qualitative Inquiry, 12(8): 5415-5424, (2021).
  • [38] Abro, A. A., Khan, A. A., Talpur, M. S. H., Kayijuka, I., “Machine Learning Classifiers: A Brief Primer”, University of Sindh Journal of Information and Communication Technology, 5(2): 63-68, (2021)
  • [39] Abro, A. A., Taşci, E., Aybars, U. G., "A Stacking-based Ensemble Learning Method for Outlier Detection", Balkan Journal of Electrical and Computer Engineering, 8(2): 181-185, (2020).
  • [40] Abro, A. A., Yimer, M. A., Bhatti, Z., "Identifying the Machine Learning Techniques for Classification of Target Datasets", Sukkur IBA Journal of Computing and Mathematical Sciences, 4(1): 45-52, (2020).
  • [41] Abro, A. A., "Vote-Based: Ensemble Approach", Sakarya University Journal of Science, 25(3): 858-866, (2021).
  • [42] Abro, A. A., Siddique, W. A., Talpur, M. S. H., Jumani, A. K., Yaşar, E., “A combined approach of base and meta learners for hybrid system”, Turkish Journal of Engineering, 7(1): 25-32, (2023).
There are 42 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Computer Engineering
Authors

Abdul Ahad Abro 0000-0002-3591-9231

Mir Sajjad Hussain Talpur 0000-0001-9897-3916

Awais Khan Jumani 0000-0001-9468-0446

Publication Date December 1, 2023
Published in Issue Year 2023 Volume: 36 Issue: 4

Cite

APA Abro, A. A., Talpur, M. S. H., & Jumani, A. K. (2023). Natural Language Processing Challenges and Issues: A Literature Review. Gazi University Journal of Science, 36(4), 1522-1536. https://doi.org/10.35378/gujs.1032517
AMA Abro AA, Talpur MSH, Jumani AK. Natural Language Processing Challenges and Issues: A Literature Review. Gazi University Journal of Science. December 2023;36(4):1522-1536. doi:10.35378/gujs.1032517
Chicago Abro, Abdul Ahad, Mir Sajjad Hussain Talpur, and Awais Khan Jumani. “Natural Language Processing Challenges and Issues: A Literature Review”. Gazi University Journal of Science 36, no. 4 (December 2023): 1522-36. https://doi.org/10.35378/gujs.1032517.
EndNote Abro AA, Talpur MSH, Jumani AK (December 1, 2023) Natural Language Processing Challenges and Issues: A Literature Review. Gazi University Journal of Science 36 4 1522–1536.
IEEE A. A. Abro, M. S. H. Talpur, and A. K. Jumani, “Natural Language Processing Challenges and Issues: A Literature Review”, Gazi University Journal of Science, vol. 36, no. 4, pp. 1522–1536, 2023, doi: 10.35378/gujs.1032517.
ISNAD Abro, Abdul Ahad et al. “Natural Language Processing Challenges and Issues: A Literature Review”. Gazi University Journal of Science 36/4 (December 2023), 1522-1536. https://doi.org/10.35378/gujs.1032517.
JAMA Abro AA, Talpur MSH, Jumani AK. Natural Language Processing Challenges and Issues: A Literature Review. Gazi University Journal of Science. 2023;36:1522–1536.
MLA Abro, Abdul Ahad et al. “Natural Language Processing Challenges and Issues: A Literature Review”. Gazi University Journal of Science, vol. 36, no. 4, 2023, pp. 1522-36, doi:10.35378/gujs.1032517.
Vancouver Abro AA, Talpur MSH, Jumani AK. Natural Language Processing Challenges and Issues: A Literature Review. Gazi University Journal of Science. 2023;36(4):1522-36.