Neural Network Methods for Natural Language Processing

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

Untitled work

(2014) · Cited by 33,419

Learning representations by back-propagating errors

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892

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 22,093

Multilayer feedforward networks are universal approximators

Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728

Regularization and Variable Selection Via the Elastic Net

Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741

Approximation by superpositions of a sigmoidal function

Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883

Learning Word and Sentence Embeddings Using a Generative Convolutional Network

Vargas-Ocampo, E., Román-Rangel, E., & Hermosillo, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-92198-3_14 · Cited by 1

Understanding the Polarity of Events in the Biomedical Literature: Deep Learning vs. Linguistically-informed Methods

Noriega-Atala, E., Liang, Z., Bachman, J. A., Morrison, C. T., & Surdeanu, M. (2019) · doi:10.18653/v1/w19-2603 · Cited by 1

Bigger versus Similar: Selecting a Background Corpus for First Story Detection Based on Distributional Similarity

Wang, F., Ross, R., & Kelleher, J. D. (2019) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-056-4_150 · Cited by 1

Interpretability for Neural Networks from the Perspective of Probability Density

Lu, L., Pan, T., Zhao, J., & Yang, J. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9002817 · Cited by 1

Elena Ferrante: A Case Study in Authorship Attribution

Savoy, J. (2020) · Springer eBooks · doi:10.1007/978-3-030-53360-1_8 · Cited by 3

Novel Approach to New Domain Aspect Identification Using Deep Learning and Word Replacement

Mampilli, B. S., & Anand, D. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-5397-4_35 · Cited by 1

Features Identification and Selection

Savoy, J. (2020) · Springer eBooks · doi:10.1007/978-3-030-53360-1_5 · Cited by 1

Muscle-Gesture Robot Hand Control Based on sEMG Signals Utilizing Deep Neural Networks

Luh, G.-C., Chiu, H.-S., & Tsai, M.-J. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-62324-1_18 · Cited by 1

Information retrieval for animal disease surveillance: a pattern-based approach.

Valentin, S., Mathieu Roche, A., & Lancelot, R. (2020) · International Workshop on Health Text Mining and Information Analysis (LOUHI) · doi:10.18653/v1/2020.louhi-1.8 · Cited by 1

KoFFI—The New Driving Experience: How to Cooperate with Automated Driving Vehicles

Erbach, R., Maurer, S., Meixner, G., Koller, M., Woide, M., Walch, M., Weber, M., Baumann, M. R. K., Grimm, P., Keber, T. O., et al. (2020) · Human-computer interaction series · doi:10.1007/978-3-030-45131-8_3 · Cited by 3

Application of Neural Network to Predict Printability of Polycaprolactone Using FDM

Narkhede, R., Karumuri, R. T., Prajapati, A. R., & Dave, H. K. (2021) · Lecture notes in mechanical engineering · doi:10.1007/978-981-33-4466-2_23 · Cited by 1

Towards human‐oriented solutions for deep semantic data analysis

Ogiela, L., & Snåšel, V. (2021) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.6252 · Cited by 1

Representing word meaning in context via lexical substitutes

Alagić, D., & Šnajder, J. (2021) · Automatika · doi:10.1080/00051144.2021.1928437 · Cited by 1

A Metaheuristic Algorithm for Classification of White Blood Cells in Healthcare Informatics

Monteiro, A. C. B., Iano, Y., França, R. P., & Arthur, R. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-70542-8_10 · Cited by 1

Thetaṣrīfin the medieval Arabic grammatical tradition

Bohas, G., & Lowenstamm, J. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198796800.003.0005 · Cited by 1

A Bibliometric Review of Methods and Algorithms for Generating Corpora for Learning Vector Word Embeddings

Sagingaliyev, B., Aitakhunova, Z., Shaimerdenova, A., Akhmetov, I., Pak, A., & Jaxylykova, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19496-2_12 · Cited by 1

Traffic control centre optimisation on South African freight corridors through intelligent weigh-in-motion

Coning, A. D., Hoffman, A. J., & Mouton, F. (2023) · South African Computer Journal · doi:10.18489/sacj.v35i1.961 · Cited by 1

KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis

Nzeyimana, A. (2023) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2023.semeval-1.98 · Cited by 1

Non-Projective Dependency Parsing via Latent Heads Representation (LHR)Preprint

Grella, M., & Cangialosi, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.02116 · Cited by 1

Analysis of EHR Free-text Data with Supervised Deep Neural Networks

Wallace, D., & Kechadi, T. (2018) · Arrow@dit (Dublin Institute of Technology) · Cited by 1

Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language UnderstandingPreprint

Luo, B., Feng, Y., Wang, Z., Huang, S., Yan, R., & Zhao, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.05588 · Cited by 1

Attending Form and Context to Generate Specialized Out-of-VocabularyWords RepresentationsPreprint

Garneau, N., Leboeuf, J.-S., Pinter, Y., & Lamontagne, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.06876 · Cited by 1

Applications of artificial intelligence in behavioral finance getting benefit from extended data sources

Liu, Y. (2020) · doi:10.20868/upm.thesis.62779 · Cited by 1

Modern Machine and Deep Learning Systems as a way to achieve Man-Computer SymbiosisPreprint

Gupta, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.10534 · Cited by 2

Analyzing natural language inference from a rigorous point of view

Salvatore, F. D. S. (2020) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.45.2020.tde-05012021-151600 · Cited by 1

Mobility-based prediction of SARS-CoV-2 spreadingPreprint

Chicchi, L., Giambagli, L., Buffoni, L., & Fanelli, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.08253 · Cited by 1

Two Timescale Hybrid Federated Learning with Cooperative D2D Local Model Aggregations.Preprint

Lin, F. P.-C., Hosseinalipour, S., Azam, S. S., Brinton, C. G., & Michelusi, N. (2021) · arXiv (Cornell University) · Cited by 1

HLA Allele Imputation with Multitask Deep Convolutional Neural NetworkPreprint

Chi, C. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.03.447012 · Cited by 1

Uncertain Talking at Central Bank’s Press Conference: News or Noise?Preprint

Hong Kong Institute for Monetary and Financial Research (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3796816 · Cited by 1

Discerning Legitimate Failures From False Alerts: A Study of Chromium's Continuous IntegrationPreprint

Haben, G., Habchi, S., Papadakis, M., Cordy, M., & Traon, Y. L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.03382 · Cited by 1

Computer Science & Information Technology (CS & IT)

(2021) · doi:10.5121/csit.2021.1108 · Cited by 1

Disentangling Hierarchical and Sequential Computations during Sentence ProcessingPreprint

Zacharopoulos, C.-N., Dehaene, S., & Lakretz, Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.07.08.499161 · Cited by 1

30 Years of Research on Semantic Similarity MeasurementPreprint

Sharma, K. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/qpb6d · Cited by 1

Lexical emergence from context : exploring unsupervised learning approaches on large multimodal language corpora

Havard, W. N. (2021) · theses.fr (ABES) · doi:10.70675/42e50a10zf9eaz4112zafc5z0b8ce968e31e · Cited by 1

Quantifying the Trade-off Between Two Types of Morphological Complexity

Cotterell, R. (2018) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/r57p8wk1

Taking Advantage of BiLSTM Encoding to Handle Punctuation in Dependency Parsing: A Brief IdeaPreprint

Grella, M. (2018) · HAL

Scalable and Efficient Probabilistic Topic Model Inference for Textual Data

Magnusson, M. (2018) · doi:10.3384/diss.diva-146964

Epilogue: Frontiers of NLP in the Deep Learning Era

Deng, L., & Liu, Y. (2018) · Springer eBooks · doi:10.1007/978-981-10-5209-5_11 · Cited by 2

Learning abstraction of a swarm to control a parent system

Crandall, K. L., Wickenheiser, A. M., & Webb, D. J. (2018) · International Conference on Control, Automation and Robotics (ICCAR) · doi:10.1109/iccar.2018.8384666

Deep machine learning for syntactic annotation projection

Marić, T. (2018)

Diffusion of Abstractive Summarisation to Improve Ease of Use and Usefulness

Makovhololo, P., Taylor, F., & Iyamu, T. (2018) · Open Innovations Conference (OI) · doi:10.1109/oi.2018.8535750

Technical notes: Syntax-aware Representation Learning With Pointer NetworksPreprint

Grella, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.07161

Saussure and structural phonology

Joseph, J. E. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198796800.003.0010

Análisis de comentarios y revisiones de usuario usando redes neuronales recurrentes y procesamiento de lenguajes naturales

León, J. B., Zambrano, F. J. R., & Plua, J. L. (2018) · Informática y Sistemas Revista de Tecnologías de la Informática y las Comunicaciones · doi:10.33936/isrtic.v2i1.1127

Iterative Self-Learning: Semi-Supervised Improvement to Dataset Volumes and Model AccuracyPreprint

Dupre, R., Fajtl, J., Argyriou, V., & Remagnin, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02823

Identifying Developers by Their Application Usage

Shulhan, I. (2019) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse-companion.2019.00070

AN ALGORITHM FOR THE RESTORATION OF PIXELS IMAGE BASED ON THE NEURAL NETWORK

Славиевич, П. В. (2019) · Proceedings of Southwest State University · doi:10.21869/2223-1560-2018-22-6-183-188

PRINCIPAL PROBLEMS OF NATURAL LANGUAGE PROCESSING SYSTEMS

Гирин, О. В. (2018) · Studia Philologica · doi:10.28925/2311-2425.2018.11.5

Text synthesis from keywords: a comparison of recurrent-neural-network-based architectures and hybrid approaches

Kolokas, N., Drosou, A., & Tzovaras, D. K. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04435-y

Content-Based Geolocation Prediction of Canadian Twitter Users and Their Tweets

Metin, A. M. (2019) · uO Research (University of Ottawa) · doi:10.20381/ruor-23751

Development of a convolution algorithm for the content of a Web resource for processing by a neural network

Gorshkov, S., Solomonov, D. V., Dvoryaninova, I., & Mihaylichenko, R. M. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/656/1/012021

Distributed Representation of Entity Mentions Within and Across Multiple Text Documents

Keshtkaran, A., Yuhaniz, S. S., & Rostami, M. R. (2019) · Open International Journal of Informatics

Update Frequency and Background Corpus Selection in Dynamic TF-IDF Models for First Story Detection

Wang, F., Ross, R., & Kelleher, J. D. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-6168-9_18

Document semantic representation: an algebraic topological approach

Meng, F. (2024) · Library, Museums and Press - UDSpace (University of Delaware) · doi:10.58088/e1yq-pm57

Automated Assessment of Norwegian L2 Essays Using Multi-task Learning

Berggren, S. J. (2019) · NORA - Norwegian Open Research Archives

Evaluating the Impact of Sub-word Information and Cross-lingual Word Embeddings on Mi’kmaq Language Modelling

Boudreau, J., Patra, A., Suvarna, A., & Cook, P. F. (2020) · Language Resources and Evaluation

Distance,Time and Terms in First Story Detection

Wang, F. (2020) · Arrow - TU Dublin (Technological University Dublin) · doi:10.21427/spp0-zx14 · Cited by 1

A Computational Linguistics Approach to Preserving and Promoting Natural Languages

Rao, D. L., Smith, E., & Gudivada, V. N. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-4029-5_35

Age-Suitability Prediction for Literature Using Deep Neural Networks

Brewer, E. (2020) · ScholarsArchive (Brigham Young University)

Issues in the Syntactic Parsing of Queries for a Natural Language Interface to Databases

Gelbukh, A. F., F., J. A. M., O., A. A. V., & Ochoa, A. (2020) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-4730-4.ch007

Author Profiling of Tweets

Savoy, J. (2020) · Springer eBooks · doi:10.1007/978-3-030-53360-1_9

Applications to Political Speeches

Savoy, J. (2020) · Springer eBooks · doi:10.1007/978-3-030-53360-1_10

TOWARDS A ROBUST UNDERSTANDING OF ARABIC WORD SENSE

Alkhatlan, A. (2019) · Digital Collections of Colorado (Colorado State University)

Montague Grammar Induction

Kim, G. L., & White, A. S. (2021) · Proceedings from Semantics and Linguistic Theory · doi:10.3765/salt.v30i0.4816

Classifier of urban objects for data from social networks

Begen, P. N., Nizomutdinov, B. A., & Tropnikov, A. S. (2020) · Naučnyj servis v seti internet · doi:10.20948/abrau-2020-41

The Methodology of Discourse Analysis: from Manuel Data Counting to Machine Learning

Filatova, O. G., Volkovskii, D., & Begen, P. N. (2020) · Naučnyj servis v seti internet · doi:10.20948/abrau-2020-28

Towards Emotion Intelligence in Neural Dialogue Systems

Huang, C. (2019) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3-p453-d634

Überwachung der Sedierung in der Endoskopie mit künstlicher Intelligenz

Garbe, J., Garbe, V., Kantelhardt, J. W., Michl, P., Eisenmann, S., Rosendahl, J., & Krug, S. (2020) · Die Gastroenterologie · doi:10.1007/s11377-020-00494-z

PredCoin: Defense against Query-based Hard-label AttackPreprint

Guo, J., Yadlapalli, Y., Thiele, L., Li, A., & Liu, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.02923

The Theory of Construct and Instrument Development Process: Supplementing Human Judgment with Natural Language Processing TechniquesPreprint

Larsen, K. R., & Sharma, R. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3719547

Progress in Deep Learning Mechanisms for Information Extraction Social Networks: An Expository Overview

Fianyi, I., & Appiah, G. A. (2021) · International Journal of Computer Applications · doi:10.5120/ijca2021921155

eQTLHap: a tool for comprehensive eQTL analysis considering haplotypic and genotypic effects

Bkhetan, Z. A., Chana, G., Ong, C. S., Goudey, B., & Ramamohanarao, K. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab093

Contactless access control system for critical objects based on deep learning neural networks

Tyutyunnik, A. A., Ивановна, Л. Е., & Lazarev, A. I. (2021) · International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE) · doi:10.1109/reepe51337.2021.9388077

Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction

Romano, M., Molà, F., & Conversano, C. (2021) · Proceedings e report · doi:10.36253/978-88-5518-304-8.29

Teaching NLP outside Linguistics and Computer Science classrooms: Some challenges and some opportunities

Vajjala, S. (2021) · Workshop on Teaching NLP (TeachingNLP) · doi:10.18653/v1/2021.teachingnlp-1.25

On Writing a Textbook on Natural Language Processing

Eisenstein, J. (2021) · Workshop on Teaching NLP (TeachingNLP) · doi:10.18653/v1/2021.teachingnlp-1.22

Evolutionary Discovery of Natural-Language Coreference Chains for Social Media AnalysisPreprint

Atkinson, J., & Escudero, A. (2021) · Research Square · doi:10.21203/rs.3.rs-562748/v1

The Potential Applications of Machine Learning-Based Artificial Neural Networking Algorithms: A Review

Channa, A., & Arif, M. M. (2021) · International Journal of Signal Processing and Analysis · doi:10.35840/2631-5114/3507

Automated reconstruction of colombian public procurement traceability

Martín, M., & Melissa, L. (2019) · instname:Universidad de los Andes

Atribuição autoral com uso de classificadores de perfis

Deutsch, C. (2020) · doi:10.11606/d.100.2020.tde-04112020-152132 · Cited by 1

Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability AssessmentPreprint

Qiu, X., Chen, Y., Chen, H., Nie, J., Shen, Y., & Lü, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.04268 · Cited by 3

Deriving Disinformation Insights from Geolocalized Twitter Callouts

Tuxworth, D., Antypas, D., Espinosa-Anke, L., Camacho-Collados, J., Preece, A. D., & Rogers, D. (2021) · ORCA Online Research @Cardiff (Cardiff University) · doi:10.48550/arxiv.2108.03067

Learning Algorithms of Sentiment Analysis

Acampa, S., Falco, C. C. D., & Trezza, D. (2021) · Advances in knowledge acquisition, transfer, and management book series/Advances in knowledge acquisition, transfer and management book series · doi:10.4018/978-1-7998-8473-6.ch012

Sentiment Weighted Word Embedding for Big Text Data

Dhanani, J., Mehta, R. G., & Rana, D. P. (2021) · International Journal of Web-Based Learning and Teaching Technologies · doi:10.4018/ijwltt.20211101.oa2

Bilingual Summarization of English and Arabic Genetic Diseases Texts

Almugbel, Z. (2021) · International Journal for Innovation Education and Research · doi:10.31686/ijier.vol9.iss9.3349

Transformer-based Relation Detect Model for Aspect-based Sentiment Analysis

Wei, Z., Xu, X., Li, L., Qin, K., & Li, L. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533453

A Rapid Tour of NLP

Palakodety, S., KhudaBukhsh, A. R., & Jayachandran, G. (2021) · SpringerBriefs in computer science · doi:10.1007/978-981-16-5625-5_3

Development of Goal-Oriented Dialogue Systems for Customer Services in Automotive Industry

Tuncer, M., Bilici, M. F., & Eryiğit, G. (2021) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista52262.2021.9548494

Redes neurais artificiais e processamento de linguagem natural aplicados a previsão do minicontrato futuro do índice ibovespa

Silva, D. E. (2021) · doi:10.31414/ee.2021.d.131368

Reducing the impact of out of vocabulary words in the translation of natural language questions into SPARQL queriesPreprint

Santana, M. A. B., Ricca, F., & Cuteri, B. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.03000 · Cited by 1

Paperswithtopic: Topic Identification from Paper Title Only

Cho, D., & Wallraven, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-02444-3_19

Text Generation and Evaluation for Human-Machine Collaborative Writing

Clark, E. (2021) · ResearchWorks at the University of Washington (University of Washington)

Überwachung der Sedierung in der Endoskopie mit künstlicher Intelligenz

Garbe, J., Garbe, V., Kantelhardt, J. W., Michl, P., Eisenmann, S., Rosendahl, J., & Krug, S. (2021) · Wiener klinisches Magazin · doi:10.1007/s00740-021-00424-x

Estudo comparativo entre abordagens estilométricas e textuais para atribuição de autoria em trabalhos escolares

dos Santos, D. C. V.-B., & Zanchettin, C. (2021) · Anais do ... Simpósio Brasileiro de Informática na Educação/Anais do Simpósio Brasileiro de Informática na Educação · doi:10.5753/sbie.2021.217413

Conclusion

Savoy, J. (2020) · Springer eBooks · doi:10.1007/978-3-030-53360-1_11

Inferência da satisfação de usuários em serviços públicos de acesso à informação utilizando aprendizado neural

Flores, A. M. (2020) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.100.2020.tde-16122020-192412

Peer Review #2 of "Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approach (v0.2)"

(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.913v0.2/reviews/2

Software-analytical tool for forecasting and evaluating the implementation of innovative processes in integration formations

(2021) · Economics and Environmental Management · doi:10.17586/2310-1172-2021-14-3-47-57

The study of variation

Fruehwald, J. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198796800.003.0027

Neural Representations of Natural Language

White, L., Togneri, R. B., Liu, W., & Bennamoun, M. (2018) · Studies in computational intelligence · doi:10.1007/978-981-13-0062-2 · Cited by 7

Feed-forward Neural Networks

Goldberg, Y. (2017) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-02165-7_4 · Cited by 1

Neural Networks with TensorFlow and Keras

Hua, P. (2024) · Apress eBooks · doi:10.1007/979-8-8688-1020-6 · Cited by 5

Real World Applications of Neural Networks in Natural Language Processing

(2018) · International Journal of Recent Trends in Engineering and Research · doi:10.23883/ijrter.2018.4064.l2ac1 · Cited by 2

Neural Networks for Natural Language Processing

Morchid, M. (2019) · HAL · Cited by 5