Natural language processing: state of the art, current trends and challenges
Open accessAbstract
Natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. In this paper, we first distinguish four phases by discussing different levels of NLP and components of N atural L anguage G eneration followed by presenting the history and evolution of NLP. We then discuss in detail the state of the art presenting the various applications of NLP, current trends, and challenges. Finally, we present a discussion on some available datasets, models, and evaluation metrics in NLP.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Intrinsic evaluation metrics solely rely on surface-form similarity and language-specific perspectives, rendering them inadequate for healthcare chatbots.
“However, they solely rely on surface-form similarity and language-specific perspectives, rendering them inadequate for healthcare chatbots.”
From Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI (2024) · cites this paper as [21]
Intrinsic metrics lack the capability to capture semantics, context, distant dependencies, semantically critical ordering change, and human perspectives, particularly in real-world scenarios.
“These metrics lack the capability to capture essential elements such as semantics19,20, context19,21, distant dependencies22,23, semantically critical ordering change21, and human perspectives, particularly in real-world scenarios.”
From Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI (2024) · cites this paper as [21]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344 · see citing works
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831 · see citing works
ASPECTS OF THE THEORY OF SYNTAX
Chomsky, N. (1964) · doi:10.21236/ad0616323 · Cited by 13,587 · see citing works
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · see citing works
Foundations of statistical natural language processing
Manning, C. D., & Schütze, H. (1999) · Cited by 9,816 · see citing works
An algorithm for suffix stripping
Porter, M. F. (1980) · Program · doi:10.1108/eb046814 · Cited by 8,096 · cited 2× · see citing works
“Knowledge discovery research use a variety of techniques to extract useful information from source documents like Parts of Speech (POS) tagging, Chunking or Shadow Parsing, Stopwords (Keywords that are used and must be removed before processing documents), Stemming (Mapping words to some base for, it has two methods,…”
On the Properties of Neural Machine Translation: Encoder–Decoder Approaches
Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) · SSST@EMNLP · doi:10.3115/v1/w14-4012 · Cited by 7,251 · see citing works
Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582924 · Cited by 6,955 · see citing works
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878 · see citing works
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058 · see citing works
Speech and language processing
Politt, R., Pollock, J., & Waller, E. (2010) · Taylor & Francis eBooks · doi:10.4324/9780203461891_chapter_3 · Cited by 3,597 · see citing works
A comparison of event models for naive bayes text classification
McCallum, A., & Nigam, K. (1998) · Cited by 3,289 · see citing works
Transformer-XL: Attentive language models beyond a fixed-length context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1285 · Cited by 3,169 · see citing works
A maximum entropy approach to natural language processing
Berger, A. L., Pietra, V. J. D., & Pietra, S. A. D. (1996) · doi:10.5555/234285.234289 · Cited by 3,083 · see citing works
The Proposition Bank: An Annotated Corpus of Semantic Roles
Palmer, M. S., Gildea, D., & Kingsbury, P. (2005) · Computational Linguistics · doi:10.1162/0891201053630264 · Cited by 2,317 · see citing works
Naive (Bayes) at forty: The independence assumption in information retrieval
Lewis, D. D. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026666 · Cited by 2,140 · see citing works
Natural language processing: an introduction
Nadkarni, P. M., Lucila Ohno-machado, A., & Chapman, W. W. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000464 · Cited by 1,984 · see citing works
A Survey of the Usages of Deep Learning for Natural Language Processing
Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836 · see citing works
Roeck, A. N. D. (1995) · John Benjamins Publishing Company eBooks · doi:10.1075/hop.m.comm2 · Cited by 1,717 · see citing works
Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
Doddington, G. R. (2002) · International Conference on Human Language Technology Research · doi:10.3115/1289189.1289273 · Cited by 1,603 · see citing works
Support vector machines for spam categorization
Drucker, H., Wu, D., & Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788645 · Cited by 1,476 · see citing works
Shallow parsing with conditional random fields
Sha, F., & Pereira, F. (2003) · Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03 · doi:10.3115/1073445.1073473 · Cited by 1,246 · see citing works
A Bayesian Approach to Filtering Junk E-Mail
Sahami, M., Dumais, S., Heckerman, D. E., & Horvitz, E. (1998) · Cited by 1,212 · see citing works
LEGAL-BERT: The Muppets straight out of Law School
Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Αλέτρας, Ν., & Androutsopoulos, I. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.261 · Cited by 1,197 · see citing works
Named Entity Recognition in Tweets: An Experimental Study
Ritter, A., Clark, S., & Etzioni, O. (2011) · Cited by 1,192 · see citing works
On the Properties of Neural Machine Translation: Encoder-Decoder ApproachesPreprint
Cho, K., van Merriënboer, B., Bahdanau, D., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.1259 · Cited by 1,112 · see citing works
Convolutional Neural Networks for Sentence ClassificationPreprint
Kim, Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5882 · Cited by 1,112 · see citing works
Generic text summarization using relevance measure and latent semantic analysis
Gong, Y., & Liu, X. (2001) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/383952.383955 · Cited by 855 · see citing works
Neural Network Methods for Natural Language Processing
Goldberg, Y. (2017) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02165-7 · Cited by 821 · see citing works
Pfam: multiple sequence alignments and HMM-profiles of protein domains
Sonnhammer, E. L. L. (1998) · Nucleic Acids Research · doi:10.1093/nar/26.1.320 · Cited by 774 · see citing works
McKeown, K. R. (1985) · Cambridge University Press eBooks · doi:10.1017/cbo9780511620751 · Cited by 773 · see citing works
Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W. (2004) · Proceedings · doi:10.1109/icdm.2003.1250949 · Cited by 729 · see citing works
Advances in Automatic Text Summarization
Liddy, E. D. (2001) · Information Retrieval · doi:10.1023/a:1011476409104 · Cited by 689 · see citing works
Advances in automatic text summarization
(2000) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(00)90017-8 · Cited by 681 · see citing works
Addressing the Rare Word Problem in Neural Machine Translation
Luong, M.-T., Sutskever, I., Le, Q. V., Vinyals, O., & Zaremba, W. (2015) · doi:10.3115/v1/p15-1002 · Cited by 626 · see citing works
Natural language processing (NLP) in management research: A literature review
Kang, Y., Cai, Z., Tan, C., Huang, Q., & Liu, H. (2020) · Journal of Management Analytics · doi:10.1080/23270012.2020.1756939 · Cited by 572 · see citing works
Developing a natural language interface to complex data
Hendrix, G. G., Sacerdoti, E. D., Sagalowicz, D., & Slocum, J. (1978) · ACM Transactions on Database Systems · doi:10.1145/320251.320253 · Cited by 536 · see citing works
Lea, W. A. (1980) · Prentice Hall PTR eBooks · Cited by 482 · see citing works
Commonsense reasoning and commonsense knowledge in artificial intelligence
Davis, E., & Marcus, G. (2015) · Communications of the ACM · doi:10.1145/2701413 · Cited by 434 · see citing works
A survey of learning-based techniques of email spam filtering
Blanzieri, E., & Bryl, A. (2008) · Artificial Intelligence Review · doi:10.1007/s10462-009-9109-6 · Cited by 407 · see citing works
Learning Rules that Classify E-Mail
Cohen, W. W. (1996) · Cited by 406 · see citing works
Green, B. F., Wolf, A. K., Chomsky, C. L., et al. (1961) · doi:10.1145/1460690.1460714 · Cited by 387 · see citing works
RoBERTa-LSTM: A Hybrid Model for Sentiment Analysis With Transformer and Recurrent Neural Network
Tan, K. L., Lee, C. P., Anbananthen, K. S. M., & Lim, K. M. (2022) · IEEE Access · doi:10.1109/access.2022.3152828 · Cited by 355 · see citing works
Covington, M. A. (1994) · Language · doi:10.2307/416341 · Cited by 345 · see citing works
Boosting Trees for Anti-Spam Email FilteringPreprint
Carreras, X., & Màrquez, L. (2001) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0109015 · Cited by 342 · see citing works
Automatic tagging of Arabic text
Diab, M., Hacıoğlu, K., & Jurafsky, D. S. (2004) · doi:10.3115/1613984.1614022 · Cited by 312 · see citing works
COVID-Twitter-BERT: A natural language processing model to analyse COVID-19 content on Twitter
Müller, M., Salathé, M., & Kummervold, P. E. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1023281 · Cited by 301 · see citing works
An Evaluation Tool for Machine Translation: Fast Evaluation for MT Research
Nießen, S., Och, F. J., Leusch, G., et al. (2000) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/56t9t4rchhar · Cited by 284 · see citing works
Lexical methods for managing variation in biomedical terminologies.
McCray, A. T., Srinivasan, S., & Browne, A. C. (1994) · PubMed · Cited by 281 · see citing works
Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based ApproachPreprint
Androutsopoulos, I., Παλιούρας, Γ., Karkaletsis, V., et al. (2000) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0009009 · Cited by 281 · see citing works
GA, MR, FFNN, PNN and GMM based models for automatic text summarization
Abdel‐Fattah, M. I., & Ren, F. (2008) · Computer Speech & Language · doi:10.1016/j.csl.2008.04.002 · Cited by 255 · see citing works
Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering
Wang, Z., Ng, P., Ma, X., Nallapati, R. M., & Xiang, B. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1599 · Cited by 246 · see citing works
Gao, T. Y., Dontcheva, M., Adar, E., Liu, Z., & Karahalios, K. G. (2015) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/2807442.2807478 · Cited by 244 · see citing works
Accelerated DP based search for statistical translation
Tillmann, C., Vogel, S., Ney, H., Zubiaga, A., & Sawaf, H. (1997) · European Conference on Speech Communication and Technology (Eurospeech) · doi:10.21437/eurospeech.1997-673 · Cited by 233 · see citing works
A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
Sakkis, G., Androutsopoulos, I., Παλιούρας, Γ., Karkaletsis, V., Spyropoulos, C. D., & Stamatopoulos, P. (2003) · Information Retrieval · doi:10.1023/a:1022948414856 · Cited by 209 · see citing works
The Representation of Meaning in the UMLS
McCray, A. T., & Nelson, S. J. (1995) · Methods of Information in Medicine · doi:10.1055/s-0038-1634592 · Cited by 205 · see citing works
Compressive Transformers for Long-Range Sequence Modelling
Rae, J. W., Potapenko, A., Jayakumar, S. M., & Lillicrap, T. P. (2019) · arXiv · doi:10.48550/arxiv.1911.05507 · Cited by 205 · see citing works
Improving the Reliability of Deep Neural Networks in NLP: A Review
Alshemali, B., & Kalita, J. K. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.105210 · Cited by 192 · see citing works
Building event-centric knowledge graphs from news
Rospocher, M., van Erp, M., Vossen, P. T. J. M., et al. (2016) · Journal of Web Semantics · doi:10.1016/j.websem.2015.12.004 · Cited by 187 · see citing works
Contextual LSTM (CLSTM) models for Large scale NLP tasksPreprint
Ghosh, S., Vinyals, O., Strope, B. P., Roy, S., Dean, T., & Heck, L. P. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.06291 · Cited by 180 · see citing works
Event Discovery in Social Media Feeds
Benson, E., Haghighi, A., & Barzilay, R. (2011) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 179 · see citing works
Multi-document summarization using sentence-based topic models
Wang, D., Zhu, S., Li, T., et al. (2009) · doi:10.3115/1667583.1667675 · Cited by 170 · see citing works
Applying regression models to query-focused multi-document summarization
Ouyang, Y., Li, W., Li, S., & Lu, Q. (2010) · Information Processing & Management · doi:10.1016/j.ipm.2010.03.005 · Cited by 165 · see citing works
Wahlster, W., & Kobsa, A. (1989) · Springer eBooks · doi:10.1007/978-3-642-83230-7_1 · Cited by 160 · see citing works
Semantics and Quantification in Natural Language Question Answering
Woods, W. A. (1978) · Advances in computers · doi:10.1016/s0065-2458(08)60390-3 · Cited by 153 · see citing works
Kobsa, A., & Wahlster, W. (1989) · Springer eBooks · doi:10.1007/978-3-642-83230-7 · Cited by 148 · see citing works
Stacking classifiers for anti-spam filtering of e-mailPreprint
Sakkis, G., Androutsopoulos, I., Παλιούρας, Γ., et al. (2001) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0106040 · Cited by 144 · see citing works
Baclic, O., Tunis, M. C., Young, K., Doan, C., & Swerdfeger, H. (2020) · Canada Communicable Disease Report · doi:10.14745/ccdr.v46i06a02 · Cited by 136 · see citing works
Evaluation metrics for generation
Bangalore, S., Rambow, O., & Whittaker, S. (2000) · doi:10.3115/1118253.1118255 · Cited by 127 · see citing works
Wong, A., Plasek, J. M., Montecalvo, S. P., & Zhou, L. (2018) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1002/phar.2151 · Cited by 127 · see citing works
An Analysis of Neural Language Modeling at Multiple ScalesPreprint
Merity, S., Keskar, N. S., & Socher, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.08240 · Cited by 125 · see citing works
Deep Learning applied to NLPPreprint
López, M. M., & Kalita, J. K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.03091 · Cited by 125 · see citing works
ifile: An Application of Machine Learning to E-Mail Filtering
Rennie, J. D. M. (2000) · Cited by 117 · see citing works
Natural language information processing
Sager, N. (1980) · Cited by 115 · see citing works
Neural Domain Adaptation for Biomedical Question Answering
Wiese, G. K., Weissenborn, D., & Neves, M. (2017) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k17-1029 · Cited by 112 · see citing works
Nation, K., Snowling, M. J., & Clarke, P. J. (2007) · International Journal of Speech-Language Pathology · doi:10.1080/14417040601145166 · Cited by 111 · see citing works
Natural Language Processing and Semantical Representation of Medical Texts
Baud, R. H., Rassinoux, A. M., & Scherrer, J. R. (1992) · Methods of Information in Medicine · doi:10.1055/s-0038-1634865 · Cited by 106 · see citing works
Machine Translation: Past, Present, Future
Russell, R. A., & Hutchins, W. J. (1987) · Modern Language Journal · doi:10.2307/328480 · Cited by 103 · see citing works
Addressing the Rare Word Problem in Neural Machine TranslationPreprint
Luong, M.-T., Sutskever, I., Le, Q. V., Vinyals, O., & Zaremba, W. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.8206 · Cited by 101 · see citing works
Baseball, an Automatic Question-Answerer
Green, B. F., Wolf, A. K., Chomsky, C. L., et al. (1899) · Defense Technical Information Center (DTIC) · doi:10.1109/afips.1961.20 · Cited by 100 · see citing works
Long story short – Global unsupervised models for keyphrase based meeting summarization
Riedhammer, K., Favre, B., & Hakkani-Tür, D. Z. (2010) · Speech Communication · doi:10.1016/j.specom.2010.06.002 · Cited by 95 · see citing works
Flexible text segmentation with structured multilabel classification
McDonald, R., Crammer, K., & Pereira, F. M. B. (2005) · doi:10.3115/1220575.1220699 · Cited by 94 · see citing works
Zajic, D. M., Dorr, B. J., & Lin, J. (2007) · Information Processing & Management · doi:10.1016/j.ipm.2007.09.007 · Cited by 93 · see citing works
Application of Convolutional Neural Network in Natural Language Processing
Wang, W., & Gang, J. (2018) · IEEE International Conference on Information Systems and Computer Aided Education (ICISCAE) · doi:10.1109/iciscae.2018.8666928 · Cited by 89 · see citing works
QCS: A system for querying, clustering and summarizing documents
Dunlavy, D., O’Leary, D. P., Conroy, J. M., et al. (2007) · Information Processing & Management · doi:10.1016/j.ipm.2007.01.003 · Cited by 86 · see citing works
Integrating Document Clustering and Multidocument Summarization
Wang, D., Zhu, S., Li, T., et al. (2011) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/1993077.1993078 · Cited by 83 · see citing works
Applying data mining techniques for descriptive phrase extraction in digital document collections
Ahonen-Myka, H., Heinonen, O., Klemettinen, M., et al. (2002) · doi:10.1109/adl.1998.670374 · Cited by 82 · see citing works
Unknown (1973) · doi:10.1145/1125118.1125149 · Cited by 79 · see citing works
Early Years in Machine Translation
(2000) · Amsterdam studies in the theory and history of linguistic science. Series 3, Studies in the history of linguistics · doi:10.1075/sihols.97 · Cited by 74 · see citing works
Wan, X. (2007) · Information Retrieval · doi:10.1007/s10791-007-9037-5 · Cited by 69 · see citing works
Sentence-Level Emotion Detection from Text Based on Semantic Rules
Seal, D. B., Roy, U. K., & Basak, R. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-7166-0_42 · Cited by 67 · see citing works
NLP methods in host-based intrusion detection systems: A systematic review and future directions
Sworna, Z. T., Mousavi, Z., & Babar, M. A. (2023) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2023.103761 · Cited by 65 · see citing works
Unsupervised Cross-Media Retrieval Using Domain Adaptation With Scene Graph
Peng, Y., & Chi, J. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2019.2953692 · Cited by 63 · see citing works
Medical Language Processing: Applications to Patient Data Representation and Automatic Encoding
Sager, N., Lyman, M. S., Nhàn, N. T., et al. (1995) · Methods of Information in Medicine · doi:10.1055/s-0038-1634579 · Cited by 61 · see citing works
Towards a standard Part of Speech tagset for the Arabic language
Zeroual, I., Lakhouaja, A., & Belahbib, R. (2017) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2017.01.006 · Cited by 61 · see citing works
Milstead, J. L. (1993) · Information Processing & Management · doi:10.1016/0306-4573(93)90064-k · Cited by 60 · see citing works
Ambiguity management in natural language generation
Kay, M., & Shemtov, H. (1997) · Cited by 58 · see citing works
Modeling latent-dynamic in shallow parsing
Sun, X., Morency, L., Okanohara, D., et al. (2008) · International Conference on Computational Linguistics · doi:10.3115/1599081.1599187 · Cited by 56 · see citing works
NLP Meets the Jabberwocky: Natural Language Processing in Information Retrieval.
Feldman, S. (1999) · Online · Cited by 50 · see citing works
Minimizing ambiguity in natural language software requirements specification
Umber, A., & Bajwa, I. S. (2011) · International Conference on Digital Information Management · doi:10.1109/icdim.2011.6093363 · Cited by 48 · see citing works
Multi-Level Knowledge Injecting for Visual Commonsense Reasoning
Zhang, W., & Peng, Y. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.2991866 · Cited by 41 · see citing works
Building Event-Centric Knowledge Graphs from NewsPreprint
Rospocher, M., van Erp, M., Vossen, P. T. J. M., et al. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3199233 · Cited by 35 · see citing works
Topic aspect-oriented summarization via group selection
Fang, H., Lü, W., Wu, F. Q., et al. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.08.031 · Cited by 34 · see citing works
Grishman, R., Sager, N., Raze, C., et al. (1973) · AFIPS National Computer Conference and Exposition (NCC) · doi:10.1145/1499586.1499693 · Cited by 31 · see citing works
Speech recognition evaluation: a review of the U.S. CSR and LVCSR programmes
Young, S. J., & Chase, L. (1998) · Computer Speech & Language · doi:10.1006/csla.1998.0101 · Cited by 31 · see citing works
A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension
Yu, S., Indurthi, S. R., Back, S., & Lee, H. (2018) · Workshop on Machine Reading for Question Answering (MRQA) · doi:10.18653/v1/w18-2603 · Cited by 31 · see citing works
Survey of the Message Understanding Conferences
Sundheim, B. M., & Chinchor, N. A. (1993) · doi:10.3115/1075671.1075684 · Cited by 30 · see citing works
Extending a natural language parser with UMLS knowledge.
McCray, A. T. (1991) · PubMed · Cited by 28 · see citing works
Xia, T. (2020) · IEEE Access · doi:10.1109/access.2020.2991328 · Cited by 28 · see citing works
The UMLS Knowledge Source server.
McCray, A. T., & Razi, A. (1995) · PubMed · Cited by 27 · see citing works
Searching Better Architectures for Neural Machine Translation
Fan, Y., Tian, F., Xia, Y., Qin, T., Li, X.-Y., & Liu, T.-Y. (2020) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2020.2995270 · Cited by 27 · see citing works
Ambiguity preserving machine translation using packed representations
Emele, M., & Dorna, M. (1998) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/980845.980907 · Cited by 26 · see citing works
Intelligent high-volume text processing using shallow, domain-specific techniques
Hayes, P. J. (1992) · L. Erlbaum Associates Inc. eBooks · Cited by 25 · see citing works
Kamp, H., & Reyle, U. (1993) · Studies in linguistics and philosophy · doi:10.1007/978-94-017-1616-1_6 · Cited by 24 · see citing works
The Role of Lexical Knowledge in Biomedical Text Understanding.
McGray, A. T., Sponsler, J. L., Brylawski, B., & Browne, A. C. (1987) · Annual Symposium on Computer Application in Medical Care · Cited by 24 · see citing works
From text to structured information
Hirschman, L., Grishman, R., & Sager, N. (1976) · AFIPS National Computer Conference and Exposition (NCC) · doi:10.1145/1499799.1499842 · Cited by 23 · see citing works
Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question AnsweringPreprint
Wang, Z., Ng, P., Ma, X., Nallapati, R. M., & Xiang, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.08167 · Cited by 23 · see citing works
A conceptual model for clinical radiology reports.
Friedman, C., Cimino, J. J., & Johnson, S. B. (1993) · PubMed · Cited by 22 · see citing works
Preserving Ambiguities in Generation via Automata Intersection
Knight, K. K., & Langkilde, I. (2000) · Cited by 20 · see citing works
Muñoz, A., Moguerza, J. M., & Martos, G. (2019) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat05041.pub2 · Cited by 20 · see citing works
Glasgow, B., Mandell, A., Binney, D., et al. (1997) · doi:10.1609/aimag.v19i1.1354 · Cited by 19 · see citing works
A Database of Computer-Structured Narrative: Methods of Computing Complex Relations.
Chi, E. C., Lyman, M. S., Sager, N., et al. (1985) · PubMed Central · Cited by 19 · see citing works
LDC-IL: The Indian repository of resources for language technology
Choudhary, N. K. (2021) · Language Resources and Evaluation · doi:10.1007/s10579-020-09523-3 · Cited by 19 · see citing works
Treebank based deep grammar acquisition and Part-Of-Speech Tagging for Sanskrit sentences
Tapaswi, N., & Jain, S. (2012) · doi:10.1109/conseg.2012.6349476 · Cited by 18 · see citing works
Shared-Private Information Bottleneck Method for Cross-Modal Clustering
Yan, X., Ye, Y., Mao, Y., et al. (2019) · IEEE Access · doi:10.1109/access.2019.2904554 · Cited by 18 · see citing works
Sharifirad, S., & Matwin, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.10584 · Cited by 18 · see citing works
Knowledge Representation of Discharge Summaries
Baud, R. H., Rassinoux, A. M., & Scherrer, J. R. (1991) · Lecture notes in medical informatics · doi:10.1007/978-3-642-48650-0_14 · Cited by 17 · see citing works
Revillard, C., BORST, F., Berthoud, M., et al. (1994) · Methods of Information in Medicine · doi:10.1055/s-0038-1635011 · Cited by 17 · see citing works
Knowledge and decisions in health telematics : the next decade
Barahona, P., & Christensen, J. P. (1994) · IOS Press eBooks · Cited by 15 · see citing works
Ogallo, W., & Kanter, A. S. (2016) · PubMed Central · Cited by 14 · see citing works
A Constituent-Centric Neural Architecture for Reading Comprehension
Xie, P., & Xing, E. P. (2017) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.18653/v1/p17-1129 · Cited by 13 · see citing works
Natural language processing of medical texts within the HELIOS environment.
Rassinoux, A. M., Michel, P.-A., Juge, C., et al. (1994) · PubMed · Cited by 12 · see citing works
Proximity Processing of Medical Text
Morel-Guillemaz, A.-M., Baud, R. H., & Scherrer, J.-R. (1990) · Lecture notes in medical informatics · doi:10.1007/978-3-642-51659-7_117 · Cited by 10 · see citing works
Resources and components for gujarati NLP systems: a survey
Desai, N. P., & Dabhi, V. K. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10120-1 · Cited by 10 · see citing works
A Medical Language Processor for Two Indo-European Languages.
Nhàn, N. T., Sager, N., Lyman, M. S., et al. (1989) · PubMed Central · Cited by 9 · see citing works
Computer-Structured Narrative in Ambulatory Care: Its Use in Longitudinal Review of Clinical Data.
Lyman, M. S., Sager, N., Friedman, C., et al. (1985) · PubMed Central · Cited by 8 · see citing works
Analysis of medical jargon: The RECIT system
Rassinoux, A. M., Juge, C., Michel, P. A., et al. (1995) · Lecture notes in computer science · doi:10.1007/3-540-60025-6_124 · Cited by 7 · see citing works
Natural Language Processing For Intelligent Information Retrieval
McCray, A. T. (2005) · doi:10.1109/iembs.1991.684392 · Cited by 7 · see citing works
Natural language processing in intelligent information retrieval
Doszkocs, T. E. (1985) · doi:10.1145/320435.320541 · Cited by 6 · see citing works
GAMES II project: a general architecture for medical knowledge-based systems
Bruno, F., Kindler, H., Leaning, M. S., et al. (1994) · Computer Methods and Programs in Biomedicine · doi:10.1016/0169-2607(94)90021-3 · Cited by 5 · see citing works
Ambiguity preserving machine translation using packed representations
Emele, M., & Dorna, M. (1998) · International Conference on Computational Linguistics · doi:10.3115/980451.980907 · Cited by 5 · see citing works
NLP Methods in Host-based Intrusion Detection Systems: A Systematic Review and Future Directions
Sworna, Z. T., Mousavi, Z., & Babar, M. A. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2201.08066 · Cited by 5 · see citing works
Medical Language Processing for Knowledge Representation and Retrievals.
Lyman, M. S., Sager, N., Ec, C., et al. (1989) · PubMed Central · Cited by 4 · see citing works
Let's Meet the Users with Natural Language Understanding
Robert, B., Laurence, A., & Lovis, C. (1994) · Studies in health technology and informatics · doi:10.3233/978-1-60750-856-4-103 · Cited by 4 · see citing works
QCS: a system for querying, clustering and summarizing documents.
Dunlavy, D., Schlesinger, J. D., O’Leary, D. P., et al. (2006) · doi:10.2172/894745 · Cited by 4 · see citing works
From text to structured information-Automatic processing of medical reports
Hirschman, L., Grishman, R., & Sager, N. (1899) · afips · doi:10.1109/afips.1976.66 · Cited by 3 · see citing works
Kamp, H., & Reyle, U. (1993) · Studies in linguistics and philosophy · doi:10.1007/978-94-011-2066-1_6 · Cited by 3 · see citing works
Walton, D. (1996) · Applied logic series · doi:10.1007/978-94-015-8632-0_1 · Cited by 1 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805
Flagged first, then by citations
Research works printed but not linked: 16
- [2]Alshawi H (1992) The core language engine. MIT press
- [7]Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In ICLR 2015
- [12]Bengio Y, Ducharme R, Vincent P (2001) A neural probabilistic language model. Proceedings of NIPS
- [17]Borst F, Sager N, Nhàn NT, Su Y, Lyman M, Tick LJ,..., Scherrer JR (1989) Analyse automatique de comptes rendus d'hospitalisation. In Degoulet P, Stephan JC, Venot A, Yvon PJ, rédacteurs. Informatique et Santé, Informatique et Gestion des Unités de Soins, Comptes Rendus du Colloque AIM-IF, Paris (pp. 246–56). [5]
- [18]Briscoe EJ Grover C Boguraev B Carroll J A formalism and environment for the development of a large grammar of English IJCAI 1987 87 703 708
- [25]Chouikhi H Chniter H Jarray F Arabic sentiment analysis using BERT model. In international conference on computational collective intelligence (pp. 621-632) 2021 Cham Springer
- [29]Collobert R, Weston J (2008) A unified architecture for natural language processing. In proceedings of the 25th international conference on machine learning (pp. 160–167)
- [59]Huang Z, Xu W, Yu K (2015) Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 · arXiv
- [66]Lass R (1998) Phonology: An Introduction to Basic Concepts. Cambridge, UK; New York; Melbourne, Australia: Cambridge University Press. p. 1. ISBN 978–0–521-23728-4. Retrieved 8 January 2011Paperback ISBN 0–521–28183-0
- [89]Morin E (1999) Automatic acquisition of semantic relations between terms from technical corpora. In proc. of the fifth international congress on terminology and knowledge engineering-TKE’99
- [105]Ranjan P, Basu HVSSA (2003) Part of speech tagging and local word grouping techniques for natural language parsing in Hindi. In Proceedings of the 1st International Conference on Natural Language Processing (ICON 2003)
- [118]Santoro A, Faulkner R, Raposo D, Rae J, Chrzanowski M, Weber T,..., Lillicrap T (2018) Relational recurrent neural networks. Adv Neural Inf Proces Syst, 31
- [124]Sharma S, Srinivas PYKL, Balabantaray RC (2016) Emotion Detection using Online Machine Learning Method and TLBO on Mixed Script. In Proceedings of Language Resources and Evaluation Conference 2016 (pp. 47–51)
- [140]"Using Natural Language Processing and Network Analysis to Develop a Conceptual Framework for Medication Therapy Management Research (2017) " AMIA... Annual Symposium proceedings. AMIA Symposium. U.S. National Library of Medicine, n.d. Web. 19 PMC5333323 28269895
- [141]Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I, (2017) Attention is all you need. In advances in neural information processing systems (pp. 5998-6008)
- [143]Walton D (1996) A pragmatic synthesis. In: fallacies arising from ambiguity. Applied logic series, vol 1. Springer, Dordrecht)
Cited by
Sorted by citations
Large language models in medicine
Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023) · Nature Medicine · doi:10.1038/s41591-023-02448-8 · Cited by 3,944 · see citing works
Chatbots: History, technology, and applications
Adamopoulou, E., & Moussiades, L. (2020) · Machine Learning with Applications · doi:10.1016/j.mlwa.2020.100006 · Cited by 1,090 · see citing works
Large Language Models for Chatbot Health Advice Studies: A Systematic Review
Huo, B., Boyle, A., Marfo, N., Tangamornsuksan, W., Steen, J. P., McKechnie, T., Lee, Y., Mayol, J., Antoniou, S. A., Thirunavukarasu, A. J., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.57879 · Cited by 283 · see citing works
Foundation metrics for evaluating effectiveness of healthcare conversations powered by generative AI
Abbasian, M., Khatibi, E., Azimi, I., Oniani, D., Shakeri Hossein Abad, Z., Thieme, A., Sriram, R., Yang, Z., Wang, Y., Lin, B., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01074-z · Cited by 213 · see citing works
“These metrics lack the capability to capture essential elements such as semantics, 29,46 context, 46,47 distant dependencies, 48,49 semantically-critical ordering change, 47 and human perspectives, particularly in real-world scenarios.”
BackgroundConversational Agents: Goals, Technologies, Vision and Challenges
Allouch, M., Azaria, A., & Azoulay, R. (2021) · Sensors · doi:10.3390/s21248448 · Cited by 168 · see citing works
Large Language Model‐Based Chatbots in Higher Education
Yigci, D., Eryilmaz, M., Yetisen, A. K., Tasoglu, S., & Ozcan, A. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202400429 · Cited by 113 · see citing works
Large language model integrations in cancer decision-making: a systematic review and meta-analysis
Hao, Y., Qiu, Z., Holmes, J., Löckenhoff, C. E., Liu, W., Ghassemi, M., & Kalantari, S. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01824-7 · Cited by 67 · see citing works
Introduction to large language models (LLMs) for dementia care and research
Treder, M. S., Lee, S., & Tsvetanov, K. A. (2024) · Frontiers in Dementia · doi:10.3389/frdem.2024.1385303 · Cited by 63 · see citing works
Detecting presence of PTSD using sentiment analysis from text data
Sawalha, J., Yousefnezhad, M., Shah, Z., Brown, M. R. G., Greenshaw, A. J., & Greiner, R. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.811392 · Cited by 58 · see citing works
Pruneski, J. A., Pareek, A., Nwachukwu, B. U., Martin, R. K., Kelly, B. T., Karlsson, J., Pearle, A. D., Kiapour, A. M., & Williams, R. J. (2023) · Knee Surgery, Sports Traumatology, Arthroscopy · doi:10.1007/s00167-022-07272-0 · Cited by 50 · see citing works
Goel, I., Bhaskar, Y., Kumar, N., Singh, S., Amanullah, M., Dhar, R., & Karmakar, S. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1550407 · Cited by 31 · see citing works
Self-citationAlanazi, S., Asif, S., & Moulitsas, I. (2024) · International Journal of Social Science and Humanity · doi:10.18178/ijssh.2024.14.2.1194 · Cited by 26 · see citing works
“NLP’s effectiveness lies in its architecture, which has two parts: Natural Language Understanding and Natural Language Generation [9].”
BackgroundThe impact of AI on modern oncology from early detection to personalized cancer treatment
Li, J., Zhang, L., Yu, Z., Bao, Z., Li, D., & Wang, L. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01276-6 · Cited by 24 · see citing works
Knowledge-guided large language model for material science
Wang, G., Hu, J., Zhou, J., Liu, S., Li, Q., & Sun, Z. (2025) · Review of Materials Research · doi:10.1016/j.revmat.2025.100007 · Cited by 16 · see citing works
Content Analysis Using Specific Natural Language Processing Methods for Big Data
Pirnau, M., Botezatu, M. A., Priescu, I., Hosszu, A., Tabusca, A., Coculescu, C., & Oncioiu, I. (2024) · Electronics · doi:10.3390/electronics13030584 · Cited by 11 · see citing works
Adoui, A. (2024) · Simulacra · doi:10.21107/sml.v7i1.21911 · Cited by 9 · see citing works
Li, Q., Koehler, S., Koenig, A., Dyrba, M., Mallik, E., Linz, N., Priller, J., Spruth, E., Altenstein, S., Wiltfang, J., et al. (2025) · Journal of Alzheimer’s Disease · doi:10.1177/13872877251359967 · Cited by 6 · see citing works
Samala, A. D., & Rawas, S. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2025.2554987 · Cited by 5 · see citing works
- Authors
- Khurana, D., Koli, A., Khatter, K., & Singh, S.
- Affiliations
- Manav Rachna International Institute of Research and Studies (IN)
- BML Munjal University (IN)
- Amity University (IN)
- Corresponding author
- Khatter, Kiran
- Venue
- Multimedia Tools and Applications
- Published
- 2022-07-14
- Volume
- 82
- Issue
- 3
- Pages
- 3713-3744
- ISSN
- 1380-7501 (ISSN-L)
- 1573-7721
- PMC
- PMC9281254
- OpenAlex
- W2747680751
- Cited by
- 1,823
- References
- 152
- Citations by year
- 2026: 251 · 2025: 583 · 2024: 560 · 2023: 301 · 2022: 50 · 2021: 48 · 2020: 22 · 2019: 4 · 2018: 3
- FWCI
- 122.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Multimedia Tools and Applications (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- natural language processing, machine translation, email spam detection, information extraction, text summarization, question answering, natural language generation, history of NLP, NLP datasets
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review