Cited by 825Natural Language Processing TechniquesTopic ModelingSpeech Recognition and Synthesis

The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.

Neural Network Methods for Natural Language Processing

Goldberg, Yoav·2017·Synthesis Lectures on Human Language Technologies

DOI 10.1007/978-3-031-02165-7

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1007/978-3-031-02165-7

Cites

108 of 108 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

The text we found for this paper looks like another document, so its claims and self-check weren’t run. The registry and citation checks still apply.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The text we found for this paper looks like another document, so the self-check wasn’t run.

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

Show all 75 references

Sorted by citations

Items 301–400 of 825

Engineering Document Summarization: A Bidirectional Language Model-Based Approach
Qiu, Y., & Jin, Y. (2022) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4054203 · Cited by 12
An integrated model for textual social media data with spatio-temporal dimensions
Diaz, J., Poblete, B., & Bravo-Márquez, F. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102219 · Cited by 16
Different Contextual Window Sizes Based RNNs for Multimodal Emotion Detection in Interactive Conversations
Lai, H., Chen, H., & Wu, S. (2020) · IEEE Access · doi:10.1109/access.2020.3005664 · Cited by 12
Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic StatePreprint
Futrell, R., Wilcox, E., Morita, T., Qian, P., Ballesteros, M., & Lévy, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03260 · Cited by 46
Requesting Help Module Interface Design on Key Partial Video with Action and Augmented Reality for Children with Autism Spectrum Disorder
Wang, C.-P., Tsai, C.-H., & Lee, Y.-L. (2022) · Applied Sciences · doi:10.3390/app12178527 · Cited by 11
Predicting Autism Spectrum Disorder in Adults Through Facial Image Analysis: A Multi-CNN with BiLSTM Model
Kumar, A., & Jaiswal, U. C. (2025) · SN Computer Science · doi:10.1007/s42979-025-03783-y · Cited by 11
Scaling up Prediction of Psychosis by Natural Language Processing
Si, D., Cheng, S. C., Xing, R., Liu, C., & Wu, H. Y. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00055 · Cited by 11
“purpose of the convolutional layers is to “extract meaningful substructures that are useful for the overall prediction task at hand” [27], and each convolutional layer”
Background
What-if I ask you to explain: Explaining the effects of perturbations in procedural text
Rajagopal, D., Tandon, N., Clark, P., Dalvi, B. B., & Hovy, E. H. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.300 · Cited by 12
Do Politicians Talk about Politics? Assessing Online Communication Patterns of Brazilian Politicians
Oliveira, L., de Melo, P. O. S. V., Amaral, M. S., & de Pinho, J. A. G. (2020) · ACM Transactions on Social Computing · doi:10.1145/3412326 · Cited by 11
Sensing Occupancy through Software: Smart Parking Proof of Concept
Rodić, L. D., Perković, T., Županović, T., & Šolić, P. (2020) · Electronics · doi:10.3390/electronics9122207 · Cited by 11
Prediction of single track clad quality in laser metal deposition using dissimilar materials: Comparison of machine learning-based approaches
Paulus, P., Ruppert, Y., Vielhaber, M., & Griebsch, J. (2023) · Journal of Laser Applications · doi:10.2351/7.0001108 · Cited by 11
Bootstrap Domain-Specific Sentiment Classifiers from Unlabeled Corpora
Mudinas, A., Zhang, D., & Mark Levene, A. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00020 · Cited by 14
Evaluating the impact of corpora used to train distributed text representation models for noisy and short texts
Lochter, J. V., Pires, P. R., Bossolani, C. A., Yamakami, A., & Almeida, T. A. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489355 · Cited by 11
“This representation has known shortcomings, such as [1]:”
Background
Polarity Classification of Twitter Messages using Audio Processing
Duşçu, M., & Günneç, D. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102346 · Cited by 11
Contextual Generation of Word Embeddings for Out of Vocabulary Words in Downstream Tasks
Garneau, N., Leboeuf, J.-S., & Lamontagne, L. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-18305-9_60 · Cited by 11
Medical knowledge infused convolutional neural networks for cohort selection in clinical trials
Chen, C., Warikoo, N., Chang, Y., Chen, J.-H., & Hsu, W.-L. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz128 · Cited by 11
A Multi-modal Deep Learning Method for Classifying Chest Radiology Exams
Nunes, N., Martins, B., da Silva, N. A., Leite, F., & Silva, M. J. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-30241-2_28 · Cited by 11
Reference Extraction from Vietnamese Legal Documents
Bach, N. X., Thủy, N. T. T., Chien, D. B., Duy, T. K., Hien, T. M., & Phuong, T. M. (2019) · International Symposium on Information and Communication Technology · doi:10.1145/3368926.3369731 · Cited by 12
Neuroevolution of Neural Network Architectures Using CoDeepNEAT and KerasPreprint
Bohrer, J. D. S., Grisci, B. I., & Dorn, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.04634 · Cited by 17
Machine learning approach for quantum non-Markovian noise classification.Preprint
Martina, S., Gherardini, S., & Caruso, F. (2021) · arXiv (Cornell University) · Cited by 10
ECC‐BERT: Classification of error correcting codes using the improved bidirectional encoder representation from transformers
Li, S., Hu, X., Huang, Z., & Zhou, J. (2022) · IET Communications · doi:10.1049/cmu2.12357 · Cited by 11
Performance Study of Origami Crash Tubes Based on Energy Dissipation History
Zhang, P., Sun, Z., Wang, H., & Xiang, X. (2022) · Energies · doi:10.3390/en15093109 · Cited by 10
Geolocating Political Events in Text
Halterman, A. (2019) · Workshop on Natural Language Processing and Computational Social Science (NLP+CSS) · doi:10.18653/v1/w19-2104 · Cited by 11
Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants
Shah, M. U., Shu, D., Prasath, V. B. S., Ni, Y., Schapiro, A. H., & Dufendach, K. R. (2021) · Applied Clinical Informatics · doi:10.1055/s-0041-1735178 · Cited by 10
Aspect-Based Sentiment Analysis in Education DomainPreprint
Hajrizi, R., & Nuçi, K. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.01429 · Cited by 10
Urdu paraphrase detection: A novel DNN-based implementation using a semi-automatically generated corpus
Iqbal, H. R., Maqsood, R., Raza, A. A., & Hassan, S. (2023) · Natural Language Engineering · doi:10.1017/s1351324923000189 · Cited by 11
Personal Data in Artificial Intelligence Systems: Natural Language Processing Technology
Ilin, I. (2024) · Journal of Digital Technologies and Law · doi:10.21202/jdtl.2024.7 · Cited by 10
IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins Using Large Language Models
Mollaei, P., Sadasivam, D., Guntuboina, C., & Farimani, A. B. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c02507 · Cited by 13
A POS Tagging Model for Vietnamese Social Media Text Using BiLSTM-CRF with Rich Features
Bach, N. X., Duy, T. K., & Phuong, T. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29894-4_16 · Cited by 9
Improving communication skills of children with autism through support of applied behavioral analysis treatments using multimedia computing: a survey
Heath, C. D. C., McDaniel, T. L., Venkateswara, H., & Panchanathan, S. (2020) · Universal Access in the Information Society · doi:10.1007/s10209-019-00707-5 · Cited by 9
Comparing High Dimensional Word Embeddings Trained on Medical Text to Bag-of-Words for Predicting Medical Codes
Yogarajan, V., Gouk, H., Smith, T. C., Mayo, M., & Pfahringer, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-41964-6_9 · Cited by 10
Quranic Topic Modelling Using Paragraph Vectors
Alshammeri, M., Atwell, E., & Alsalka, M. A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-55187-2_19 · Cited by 12
A Sentiment-based Hotel Review Summarization using LSTM Neural Networks
Bompotas, A., Ilias, A., Adamopoulos, M., Kanavos, A., Makris, C. H., Rompolas, G., & Savvopoulos, A. (2020) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa50023.2020.9284397 · Cited by 9
Sentiment analysis using an ensemble approach of BiGRU model: A case study of AMIS tweets
Hameed, Z., Shapoval, S., García-Zapirain, B., & Zorrilla, A. M. (2020) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit51521.2020.9408866 · Cited by 9
Boosting a Rule-Based Chatbot Using Statistics and User Satisfaction Ratings
Efraim, O., Maraev, V., & Rodrigues, J. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-71746-3_3 · Cited by 9
Lörres, Möppes, and the Swiss. (Re)Discovering regional patterns in anonymous social media data
Purschke, C., & Hovy, D. (2019) · Journal of Linguistic Geography · doi:10.1017/jlg.2019.10 · Cited by 9
Deep Learning in Natural Language Processing, edited by Li Deng and Yang Liu. Singapore: Springer, 2018. ISBN 9789811052088. XVII + 329 pages
Feng, H., & Shi, F. (2019) · Natural Language Engineering · doi:10.1017/s1351324919000597 · Cited by 9
Assessing The Importance Of Colours For CNNs In Object RecognitionPreprint
Singh, A., Bay, A., & Mirabile, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.06917 · Cited by 14
AI Watch: Assessing Technology Readiness Levels for Artificial IntelligencePreprint
Fernando, M. P., Emilia, G. G., & Hernández‐Orallo, J. (2020) · RePEc: Research Papers in Economics · doi:10.2760/15025 · Cited by 11
“— Communication: Natural Language Processing (NLP) is the AI subfield concerned with the research of efficient mechanisms for communication between humans and machines through natural language (Clark et al. 2013, Goldberg 2017).”
Background
Perils and Opportunities in Using Large Language Models in Psychological ResearchPreprint
Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J. P., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/tg79n · Cited by 10
Effectiveness of Logistic Regression for Sentiment Analysis of Tweets About the Metaverse
Salloum, S. A., Alfaisal, R., Basiouni, A., Shaalan, K. F., & Salloum, A. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-65996-6_3 · Cited by 8
Mortality forecasting via multi-task neural networks
Mori, L. D., Haberman, S., Millossovich, P., & Zhu, R. (2025) · Astin Bulletin · doi:10.1017/asb.2025.10 · Cited by 8
“…. , X L ) that can be numerical or categorical, or a combination of them, and the corresponding output Y , we have to focus on the hyperparameters of the NNs, that is those settings that have to be set before the parameters are learnt in the training process, see Goldberg (2017) and Prince (2023).”
Empowering text classification with NLP and explainable AI for enhanced interpretability
Mustafa, S. K., & Saeed, M. H. (2025) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-025-00273-2 · Cited by 8
The (early) history of sign language phonology
van der Hülst, H. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198796800.003.0014 · Cited by 7
Disciplines of AI: An Overview of Approaches and Techniques
Häuselmann, A. (2022) · Information technology and law series/Information technology & law series · doi:10.1007/978-94-6265-523-2_3 · Cited by 9
Recurrent Spectral Network (RSN): Shaping a discrete map to reach automated classification
Chicchi, L., Fanelli, D., Giambagli, L., Buffoni, L., & Carletti, T. (2023) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2023.113128 · Cited by 8
Towards automating self-admitted technical debt repayment
Alhefdhi, A., Dam, H. K., & Ghose, A. (2023) · Information and Software Technology · doi:10.1016/j.infsof.2023.107376 · Cited by 8
Family member information extraction via neural sequence labeling models with different tag schemes
Dai, H.-J. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0996-4 · Cited by 8
Integrating lexical and prosodic features for automatic paragraph segmentation
Lai, C., Farrús, M., & Moore, J. D. (2020) · Speech Communication · doi:10.1016/j.specom.2020.04.007 · Cited by 7
Multilayer heuristics based clustering framework (MHCF) for author name disambiguation
Waqas, H., & Qadir, M. A. (2021) · Scientometrics · doi:10.1007/s11192-021-04087-7 · Cited by 8
Towards estimating global probabilities of evaluation in English based on automatic extraction of least delicate Appraisal in large corpora
Almutairi, B. A. A. (2021) · Language Sciences · doi:10.1016/j.langsci.2021.101432 · Cited by 8
Deep parameterizations of pairwise and triplet Markov models for unsupervised classification of sequential data
Gangloff, H., Morales, K., & Petetin, Y. (2022) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2022.107663 · Cited by 9
“…to their great performances for several applications such that speech recognition [Deng et al., 2013, Chan et al., 2016, Abdel-Hamid et al., 2013], image recognition [Fu et al., 2017, Traore et al., 2018, Zheng et al., 2017], natural language processing [Collobert and Weston, 2008, Goldberg, 2017].”
A Scoping Review of Current Developments in the Field of Machine Learning and Artificial Intelligence
Rajamani, S. K., & Iyer, R. S. (2023) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-6684-8582-8.ch009 · Cited by 8
Drink2Vec: Improving the classification of alcohol-related tweets using distributional semantics and external contextual enrichment
Grzeça, M. A., Becker, K. E., & Galante, R. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102369 · Cited by 8
Mixtures of Deep Neural Experts for Automated Speech Scoring
Papi, S., Trentin, E., Gretter, R., Matassoni, M., & Falavigna, D. (2020) · Interspeech (USB) · doi:10.21437/interspeech.2020-1055 · Cited by 14
A Systematic Study of Leveraging Subword Information for Learning Word RepresentationsPreprint
Zhu, Y., Vulić, I., & Korhonen, A. (2019) · arXiv (Cornell University) · doi:10.17863/cam.39780 · Cited by 8
Machine Learning Algorithm’s Measurement and Analytical Visualization of User’s Reviews for Google Play StorePreprint
Karim, A., Azhari, A., Belhouari, S. B., & Qureshi, A. A. (2020) · Preprints.org · doi:10.20944/preprints202003.0249.v1 · Cited by 8
Perils and Opportunities in Using Large Language Models in Psychological ResearchPreprint
Abdurahman, S., Atari, M., Karimi-Malekabadi, F., Xue, M. J., Trager, J. P., Park, P. S., Golazizian, P., Omrani, A., & Dehghani, M. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/d695y · Cited by 8
Predictive Analytics for Customer Behavior
Rosário, A. T., Cruz, R. M. N., Moniz, L. B., & Casaca, J. A. (2024) · Advances in marketing, customer relationship management, and e-services book series · doi:10.4018/979-8-3693-3455-3.ch002 · Cited by 7
Reachability analysis of recurrent neural networks
Choi, S.-W., Li, Y., Yang, X., Yamaguchi, T., Hoxha, B., Fainekos, G., Prokhorov, D., & Tran, H.-D. (2025) · Nonlinear Analysis. Hybrid Systems · doi:10.1016/j.nahs.2025.101581 · Cited by 9
Predicting clinical trial duration via statistical and machine learning models
Cho, J., Xu, Q., Wong, C. H., & Lo, A. W. (2025) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2025.101473 · Cited by 7
Employing natural language processing to build a knowledge graph in the realm of additive manufacturing
Haruna, A., Noman, K., Li, Y., Li, L., Wang, X., Ashfak, K., & Alhassan, A. B. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02628-y · Cited by 8
CASIE – Computing affect and social intelligence for healthcare in an ethical and trustworthy manner
Vasiliu, L., Cortis, K., McDermott, R., Kerr, A., Peters, A., Hesse, M., Hagemeyer, J., Belpaeme, T., McDonald, J., Villing, R. C., et al. (2021) · Paladyn Journal of Behavioral Robotics · doi:10.1515/pjbr-2021-0026 · Cited by 7
A Comprehensive Overview of Large Language Models (LLMs)
Uysal, M., Uysal, A., & -, Y. K. (2025) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2025.v07i01.34609 · Cited by 7
Learning from Few Samples: Lexical Substitution with Word Embeddings for Short Text Classification
Elekes, Á., Stefano, A. S. D., Schäler, M., Böhm, K., & Keller, M. (2019) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1109/jcdl.2019.00025 · Cited by 7
Vector Symbolic Architectures for Context-Free Grammars
Graben, P. B., Huber, M., Meyer, W., Römer, R., & Wolff, M. (2021) · Cognitive Computation · doi:10.1007/s12559-021-09974-y · Cited by 8
“Beginning with hard-wired recurrent neural architectures [33, 34, 35, 28], the advent of deep learning algorithms lead to state-of-the-art language processing through recursive neural networks (RNN, [36]), through long-short-term memory networks (LSTM, [37, 38]), and through convolutional neural networks (CNN, [8, 39]…”
Background
Time2Vec Embedding on a Seq2Seq Bi-directional LSTM Network for Pedestrian Trajectory Prediction
Peñaloza, V. (2020) · Research in Computing Science · Cited by 7
Shallow over Deep Neural Networks: A Empirical Analysis for Human Emotion Classification Using Audio Data
Kanani, C. S., Gill, K., Behera, S., Choubey, A., Gupta, R. K., & Misra, R. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-76736-5_13 · Cited by 7
Diacritics generation and application in hate speech detection on Vietnamese social networks
Le-Hong, P. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107504 · Cited by 7
The Resolved Mutual Information Function as a Structural Fingerprint of Biomolecular Sequences for Interpretable Machine Learning Classifiers
Bohnsack, K. S., Kaden, M., Abel, J., Saralajew, S., & Villmann, T. (2021) · Entropy · doi:10.3390/e23101357 · Cited by 7
A Data-Driven Model for Automated Chinese Word Segmentation and POS Tagging
Xu, Q., & Wang, Z. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7622392 · Cited by 7
RegBR: A novel Brazilian government framework to classify and analyze industry-specific regulations
Valle, L. M., Dantas, S. G., Silva, D. G., Dias, U. S., & Monastério, L. M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0275282 · Cited by 11
“These architecture were chosen as they are often used in NLP applications [41] and are also some of the building blocks of more complex deep learning models.”
Dialog Acts Classification for Question-Answer Corpora.
Chakravarty, S., Chava, R. V. S. P., & Fox, E. A. (2019) · Cited by 7
ADAPT at SemEval-2018 Task 9: Skip-Gram Word Embeddings for Unsupervised Hypernym Discovery in Specialised Corpora
Maldonado, A., & Klubička, F. (2018) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s18-1151 · Cited by 7
A Neurobiologically Motivated Analysis of Distributional Semantic ModelsPreprint
Utsumi, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.01830 · Cited by 7
Improving complex sale cycles and performance by using machine learning and predictive analytics to understand the customer journey
Adam, M. B. (2018) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 7
Biomedical Concept Recognition Using Deep Neural Sequence ModelsPreprint
Hailu, N., Bada, M. A., Hadgu, A. T., & Hunter, L. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/530337 · Cited by 6
Extracting Weighted Finite Automata from Recurrent Neural Networks for Natural Languages
Wei, Z., Zhang, X., & Sun, M. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-17244-1_22 · Cited by 6
Evaluating Text GANs as Language Models
Tevet, G., Habib, G., Shwartz, V., & Berant, J. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1233 · Cited by 12
An efficient optical-based binary neural network hardware accelerator for harsh environments
Jahannia, B., Ye, J.-C., Altaleb, S., Patil, C., Heidari, E., & Dalir, H. (2024) · OPTO · doi:10.1117/12.3003287 · Cited by 12
Short-term prediction of bank deposit flows: do textual features matter?
Katsafados, A. G., & Anastasiou, D. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06048-8 · Cited by 6
“Considering their ability to efficiently deal with textual information due to the non-linearity they offer, artificial neural networks (ANN) are used in a broad spectrum of tasks in the Nat - ural Language Processing (NLP) domain (Goldberg, 2017 ).”
Banks’ stock price crash risk prediction with textual analysis: a machine learning approach
Anastasiou, D., Katsafados, A. G., & Tzomakas, C. (2025) · Annals of Operations Research · doi:10.1007/s10479-025-06567-y · Cited by 6
“So far, artiﬁcial neural networks are part of machine learning models, which are frequently used in the domain of Natural Language Processing (Goldberg, 2017).”
Detecting Burnout Among Undergraduate Computing Students with Supervised Machine Learning
Yeskuatov, E., Foo, L. K., & Chua, S.-L. (2025) · Healthcare · doi:10.3390/healthcare13233182 · Cited by 6
“The relationship can be expressed as Y = σ ( Xw + b ) , where X is the set of student characteristics, w is the set of weights assigned to each student characteristic, and b represents the bias term [41].”
An Overview of Artificial Intelligence Technology Directed at Smart Sensors and Devices from a Modern Perspective
Monteiro, A. C. B., França, R. P., Arthur, R., & Iano, Y. (2021) · Studies in big data · doi:10.1007/978-3-030-77214-7_1 · Cited by 6
Deep Learning Applications for Interactive Marketing in the Contemporary Digital Age
Yu, B. T. W. (2023) · Springer eBooks · doi:10.1007/978-3-031-14961-0_31 · Cited by 6
Text Analytics in Modern Healthcare using NLP
Kaur, K., Suman, S., & Jaswal, R. (2023) · International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) · doi:10.1109/icacite57410.2023.10182715 · Cited by 6
“Each word with its count is then treated as its unique feature [15].”
Background
Stable Linear Subspace Identification: A Machine Learning Approach
Natale, L. D., Zakwan, M., Svetozarevic, B., Heer, P., Giancarlo Ferrari-trecate, A., & Jones, C. N. (2024) · European Control Conference (ECC) · doi:10.23919/ecc64448.2024.10590843 · Cited by 10
Refining Pretrained Word Embeddings Using Layer-wise Relevance Propagation
Utsumi, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1520 · Cited by 6
A Neural Graph-based Approach to Verbal MWE Identification
Waszczuk, J., Ehren, R., Stodden, R., & Kallmeyer, L. (2019) · Workshop on Multiword Expressions (MWE) · doi:10.18653/v1/w19-5113 · Cited by 6
Evaluating Neural Model Robustness for Machine Comprehension
Wu, W. M. C., Arendt, D., & Volkova, S. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.210 · Cited by 6
Information Bottleneck in Deep Learning - A Semiotic Approach
Muşat, B., & Andonie, R. (2022) · International Journal of Computers Communications & Control · doi:10.15837/ijccc.2022.1.4650 · Cited by 6
Endowing Intelligent Vehicles with the Ability to Learn User’s Habits and Preferences with Machine Learning Methods
Barbosa, P., Ferreira, F., Fernandes, C., Erlhagen, W., Guimarães, P., Wojtak, W., Monteiro, S., & Bicho, E. G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21753-1_16 · Cited by 6
Weighted automata extraction and explanation of recurrent neural networks for natural language tasks
Wei, Z., Zhang, X., Zhang, Y., & Sun, M. (2023) · Journal of Logical and Algebraic Methods in Programming · doi:10.1016/j.jlamp.2023.100907 · Cited by 6
A hybrid artificial intelligence framework for enhancing digital forensic investigations of infotainment systems
Fayyaz, Y., Almehmadi, A., & El‐Khatib, K. (2024) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2024.301751 · Cited by 7
CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model
Mai, F., Galke, L. P. A., & Ansgar Scherp, A. (2019) · Open Access at Essex (University of Essex) · doi:10.48550/arxiv.1902.06423 · Cited by 6
Sentiment Analysis using Novel Distributed Word Embedding for Movie Reviews
Dhanani, J., Mehta, R. G., Rana, D. P., & Tidke, B. (2018) · International Conference on Advanced Computing · doi:10.1109/icoac44903.2018.8939104 · Cited by 6
Methodology for Analyzing the Traditional Algorithms Performance of User Reviews Using Machine Learning Techniques
Karim, A., Azhari, A., Belhouari, S. B., Qureshi, A. A., & Ahmad, M. (2020) · Algorithms · doi:10.3390/a13080202 · Cited by 6
A Review on Natural Language Processing: Back to Basics
Dinesh, P. M., Sujitha, V., Salma, C., & Srijayapriya, B. (2021) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-15-9651-3_54 · Cited by 6
Analyzing API Sequences for Malware Monitoring Using Machine Learning
Voronin, V., & Morozov, A. A. (2021) · International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA) · doi:10.1109/summa53307.2021.9632005 · Cited by 6
A Semantic Annotation Pipeline towards the Generation of Knowledge Graphs in Tribology
Kügler, P., Marian, M., Dorsch, R., Schleich, B., & Wartzack, S. J. (2022) · Lubricants · doi:10.3390/lubricants10020018 · Cited by 7

Authors: Goldberg, Y.
Affiliations: Bar-Ilan University (IL)
Corresponding author: Goldberg, Yoav
ORCID: Goldberg, Yoav
Venue: Synthesis Lectures on Human Language Technologies
Publisher: Springer International Publishing
Published: 2017
ISSN: 1947-4040 (ISSN-L)
1947-4059
DOI: 10.1007/978-3-031-02165-7
OpenAlex: W2606089314
Semantic Scholar: 6c27d9844fcb94dfcd6decab7df559134d6d4fd7
Cited by: 821
References: 75
Citations by year: 2026: 38 · 2025: 72 · 2024: 60 · 2023: 101 · 2022: 111 · 2021: 134 · 2020: 142 · 2019: 92 · 2018: 64 · 2017: 6
FWCI: 37.46
Type: book
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Synthesis lectures on human language technologies (publishedVersion)
CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
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 Recognition and Synthesis
Keywords: artificial neural networks, natural language processing, machine learning models

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

Neural Network Methods for Natural Language Processing

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks