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 701–800 of 825

Nineteenth-century historical linguists’ contributions to phonology
Salmons, J. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198796800.003.0008
Writing systems
Sproat, R. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198796800.003.0002 · Cited by 1
The East Asian tradition
Duanmu, S., & Kubozono, H. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198796800.003.0004
Detecção de anomalias em logs de eventos de processos de negócio: um estudo comparativo entre abordagens baseadas em redes neurais e baseadas em contagem
Krugger, E. M. R. (2022) · doi:10.11606/d.100.2022.tde-04052022-215626
Introduction
Kim, J.-Y. (2022) · Springer eBooks · doi:10.1007/978-3-030-98781-7_1
A Novel Integrated Learning Scheme for Predictive Diagnosis of Critical Care燩atient
Khope, S. R., & Elias, S. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.029423
The Role of Machine Learning in Astronomy
Saraswat, P., & Jain, D. (2021) · International Journal of Advanced Engineering Research and Applications · doi:10.46593/ijaera.2021.v07i03.001
Learning Phonotactics in a Differentiable Framework of Subregular Languages
Dai, H., & Futrell, R. (2022) · Proceedings of the Annual Meetings on Phonology · doi:10.3765/amp.v9i0.5173
“Furthermore, modern ‘deep learning’ approaches to machine learning are based almost entirely on gradient descent algorithms (Goldberg, 2017).”
Background
A Temporal Ontology Guided Clustering Methodology with a Case Study on Detection and Tracking of Artificial Intelligence TopicsPreprint
Vahidnia, S., Abbasi, A., & Abbass, H. A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4200134
Analysis, Design and Evaluation of a High-Performance Stochastic Multilayer Perceptron: from Mini-Batch Training to InferencePreprint
Wang, Z., Niknia, F., Liu, S., Reviriego, P., & Lombardi, F. (2022) · doi:10.36227/techrxiv.21251418
Diretrizes para a compatibilização de SOCs com vistas a uma recuperação inteligente da informação
Barbosa, N. T., & Campos, M. L. D. A. (2022) · Scire representación y organización del conocimiento · doi:10.54886/scire.v28i2.4808
B 12 Automatische Sprachverarbeitung
Hahn, U. (2022) · De Gruyter eBooks · doi:10.1515/9783110769043-024
Pengaruh Distribusi Panjang Data Teks pada Klasifikasi: Sebuah Studi Awal
Faraby, S. A., & Romadhony, A. (2022) · JURNAL MEDIA INFORMATIKA BUDIDARMA · doi:10.30865/mib.v6i3.4259
“Metode machine learning yang dulu menjadi mainstream seperti KNN [14], Naive Bayes, SVM, Random Forest saat ini sudah dikalahkan oleh metode deep learning [15], misalnya CNN, utamanya pada kondisi dengan data yang banyak [16].”
Method
LITERATURE REVIEW OF DIFFERENT MODELS OF CONTENT GENERATION
(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets33369
Automatic question generation from natural language texts
Cao, Z. (2022) · doi:10.32657/10356/164625
NLP (Natural Language Processing) and NLP (Natural Language Programming) Using Decision Making Test and Evaluation Laboratory (DEMATEL) Method
(2019) · doi:10.46632/cllem/1/5/15
Text Data Pre-Processing for Time-series Modelling
Poměnková, J., Koráb, P., & Štrba, D. (2023) · International Conference Radioelektronika · doi:10.1109/radioelektronika57919.2023.10109034
Formal Models of Neural Network and Deep Learning
Feng, Z. (2023) · Springer eBooks · doi:10.1007/978-981-16-5172-4_11
CONTENT GENERATION USING AI
(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets40811
Research on the evolution of neural networks models in NLP
Zhao, Z. (2023) · Applied and Computational Engineering · doi:10.54254/2755-2721/4/2023385
Comparison of Novel Recurrent Neural Network Over Artificial Neural network in Predicting Email spammers with improved accuracy
Neeharika, C., & Kalaiarasi, S. (2023) · E3S Web of Conferences · doi:10.1051/e3sconf/202339904025
“The inner layers are all hidden and formed by units that change the information received from layer to layer adaptively through a series of transformations (Goldberg 2017).”
Background
Evaluating Transformers’ Sensitivity to Syntactic Embedding Depth
Hao, H. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-38318-2_16
Should you marginalize over possible tokenizations?
Chirkova, N., Kruszewski, G., Rozen, J., & Dymetman, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.1 · Cited by 6
Sentence and Document Representation Learning
Ding, N., Lin, Y., Liu, Z., & Sun, M. (2023) · Springer eBooks · doi:10.1007/978-981-99-1600-9_4 · Cited by 1
Basic Principles of Cross-Lingual Models
Sharoff, S., Rapp, R., & Zweigenbaum, P. (2023) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-31384-4_2
Induction of Bilingual Dictionaries
Sharoff, S., Rapp, R., & Zweigenbaum, P. (2023) · Synthesis lectures on human language technologies · doi:10.1007/978-3-031-31384-4_5
Nested perceptron model and its learning algorithm
Huang, X., Liu, Z., Liu, H., & Deng, Z. (2023) · International Conference on Frontiers of Electronics, Information and Computation Technologies · doi:10.1109/icfeict59519.2023.00056
Generating Control Command for an Autonomous Vehicle Based on Environmental Information
Farahani, F. A., Shouraki, S. B., & Dastjerdi, Z. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-43763-2_12
Instance Weighting in Neural Networks for Click-Through Rate Prediction
Biçici, E. (2023) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu58738.2023.10296657
Knowledge-aware representation learning for natural language processing applications
Zhang, J. (2023) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/171068
РОЗРОБКА ДОДАТКУ ДЛЯ ФОРМУВАННЯ ТЕСТОВИХ ЗАВДАНЬ З ГРАМАТИКИ АНГЛІЙСЬКОЇ МОВИ НА ОСНОВІ НЕЙРОННИХ ТЕХНОЛОГІЙ
Zavgorodnii, V., Завгородня, Г. А., Валявська, Н. О., & Даріков, Д. О. (2023) · Таврійський науковий вісник Серія Технічні науки · doi:10.32782/tnv-tech.2023.4.5
Mining for meaning: how text mining can uncover the French Liberal School’s key ideas
Borja, F. A. (2023) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqad084
Pourquoi les fonctions lexicales seraient-elles acceptables pour le Traitement Automatique des Langues ?
عبد, ا. أ. ع. ا. (2023) · Maǧallaẗ Kulliyyaẗ Al-Adāb - Ǧamiʿāẗ Sūhāğ /Maǧallaẗ Kulliyyaẗ Al-Adāb - Ǧamiʿāẗ Sūhāğ · doi:10.21608/bfa.2023.248936.1272
Recommending Related Research Papers by Topic Modelling
van Heerden, J., Haskins, B., & Botha, R. A. (2023) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet58911.2023.10389606
Use ChatGPT to maximize everyday efficiency
Shi, D. (2024) · Naturalis scientias. · doi:10.62252/nss.2024.1006 · Cited by 1
A phrase-based questionnaire–answering approach for automatic initial frailty assessment based on clinical notes
Wijesinghe, Y. V., Xu, Y., Li, Y., & Zhang, Q. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108043 · Cited by 1
Natural Language Processing (NLP): Enabling Machines to Understand and Process
Gupta, A. K. (2024) · International Journal of Food and Nutritional Sciences · doi:10.48047/ijfans/09/03/29
Unlocking Digital Gates: The Essence of Website Authentication Using Natural Language Processing
Sumathi, P. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.60360
Unveiling Text Representation with 'Bag of Words'
Jain, M., Jain, S., Daga, S., & Roshni (2024) · International Journal of Scientific Research in Computer Science Engineering and Information Technology · doi:10.32628/cseit2410314
Lemmatization of Nouns in the Azerbaijani Language using Embeddings
Aghayev, A. F., & Molodyakov, S. A. (2024) · IEEE International Conference on Soft Computing and Measurements (SCM) · doi:10.1109/scm62608.2024.10554117
To Study the Comparative Analysis of Classification Algorithms for Heart Stroke Prediction
Darde, M. S., & Wagh, M. S. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-18926
The Use of ChatGPT in Acquiring Technical Term for Report Writing among Aircraft Maintenance Students
Thambirajah, J. I. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i03.23153
Cognition of Time and Thinking Beyond
Bi, Z. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-60183-5_10
Natural Language Processing for a Personalised Educational Experience in Virtual Reality
Alghamdi, N., & Cristea, A. I. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-64312-5_43
Avaliação de Classificadores na Análise de Sentimentos em Redes Sociais Durante a Pandemia da COVID-19
Malakin, L. A. (2024) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.55.2024.tde-05072024-112257
Using Large Language Models to Facilitate the Utilization of Specific Application Programming Interfaces in Learning Factories
Dorka, F., Otmani, K. E., Hentsch, M., Künster, N., & Palm, D. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-65400-8_40
A method to shorten the time of high-dimensional matrix operations and convolution operations
Zhang, H. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/86/20241551
A Study of Improvement Strategies for Semantic Analysis Techniques in Natural Language Processing
Wang, P., & Ma, Z. (2024) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2024-3266
Can central bankers' talk predict bank stock returns? A machine learning approachPreprint
Katsafados, A. G., Leledakis, G. N., Pyrgiotakis, E. G., & Panagiotou, N. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5022604
157Chapter 7 Natural Language Processing
(2026) · De Gruyter eBooks · doi:10.1515/9783112217122-007
The impact of emotional valence on students learning performance and evaluation: A text mining of students' opinion data
OKOYE, K. O. (2024) · International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · doi:10.1109/iceccme62383.2024.10796989
“Whilst BoW [34] is concerned with known words in a textual document, the IDF of a word denotes how common or sporadic a word is in a corpus [19].”
List of figures
(2025) · Contact language library · doi:10.1075/coll.62.lof
References
(2025) · Contact language library · doi:10.1075/coll.62.refs
Conclusion
Gonzales, W. D. W. (2025) · Contact language library · doi:10.1075/coll.62.c8
The sociolinguistic situation of the Lannangs
(2025) · Contact language library · doi:10.1075/coll.62.c2
Appendix D. Statistical tables for Chapter 5
(2025) · Contact language library · doi:10.1075/coll.62.app4
Examining syntactic variation further
(2025) · Contact language library · doi:10.1075/coll.62.c6
A variationist sketch grammar of Lánnang‑uè
(2025) · Contact language library · doi:10.1075/coll.62.c3
Variability in “Lánnang‑uè” as a language practice and repertoire
(2025) · Contact language library · doi:10.1075/coll.62.c7
Examining lexical variation further
(2025) · Contact language library · doi:10.1075/coll.62.c5
Index
(2025) · Contact language library · doi:10.1075/coll.62.index
Introduction
(2025) · Contact language library · doi:10.1075/coll.62.c1
List of tables
(2025) · Contact language library · doi:10.1075/coll.62.lot
Examining phonological variation further
(2025) · Contact language library · doi:10.1075/coll.62.c4
Table of contents
(2025) · Contact language library · doi:10.1075/coll.62.toc
Appendix B. Interview questions
(2025) · Contact language library · doi:10.1075/coll.62.app2
Appendix A. Sample wordlist for stress and tone production experiment
(2025) · Contact language library · doi:10.1075/coll.62.app1
Appendix C. Supplementary linear regression results — stress perception
(2025) · Contact language library · doi:10.1075/coll.62.app3
Verifying Robustness of Neural Networks with Abstract Features
Li, X., & Xia, H. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10831737
Prelim pages
(2025) · Contact language library · doi:10.1075/coll.62.prelim
A Study of Imagery Translation Strategies in English and American Poetry Aided by Natural Language Processing Technology
Lei, Y. (2025) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2025-0479
AI-Powered Text Synthesis Platform: Innovating Digital Content Development with A Full-Stack SAAS Solution
Gurav, S. S., Mayekar, S., Hodage, A., Chavan, P., Mangutte, M., & Kamate, S. (2025) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-23922
Banking Bot for Automated Operations and Customer Support
Raja, N. V. M. K., Pavan, J. S., Kumar, K., Karthik, M., Vamsi, P. C., Naveen, M., & Sathwik, V. S. (2025) · doi:10.38124/ijisrt/25apr658
Category data embedding
Wang, W. (2025) · doi:10.26481/dis.20250522ww
LLMs as Your Second Brain - How AI Will Affect the Way We Solve Problems
Speicher, P., & Becker, L. (2025) · Springer proceedings in business and economics · doi:10.1007/978-3-031-88052-0_37
BRIDGING RULE-BASED PRECISION WITH AI INTELLIGENCE: ENHANCING NLP WITH REGULAR EXPRESSIONS AND MACHINE LEARNING
M, A. V., Arjunan, R., Divyasree, T., Manigandan, R., Muruganantham, V., Venkata Balaji, R., & Nilan, M. (2025) · Iterative International Publishers eBooks · doi:10.58532/nbennuraimlsw4
A Machine Learning Approach to Analyze Patterns and Comments in Java Code using Python
Sharma, K., Gupta, M., Ahuja, R., Prawar, P., & Kumari, M. (2025) · WSEAS Transactions on Information Science and Applications archive · doi:10.37394/23209.2025.22.54
A reviewer identification using machine learning methods
Bolshakov, D. Y. (2025) · Science Editor and Publisher · doi:10.24069/sep-25-35
Beyond the Algorithm
Khari, M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2670-2.ch015 · Cited by 2
Transformative Potential of NLP-Enabled AI in Advancing Applied Science and Technology
Seetharaman, S., Nalakath, P. M., Parambath, A. K., & Joseph, P., V (2025) · International Conference on Networks & Advances in Computational Technologies (NetACT) · doi:10.1109/netact65906.2025.11188264
“Neural network models — especially recurrent neural networks (RNNs), convolutional neural networks (CNNs), and more recently, transformer-based architectures — have introduced higher levels of abstraction and contextual awareness in language modeling [14].”
Computational Modeling of Second Language Learning
Zhao, X., & Li, P. (2025) · Wiley eBooks · doi:10.1002/9781405198431.wbeal20371
Analysis of Verbs as Linguistic Markers in Political Disinformation
Calderón, J. C., Puraivan, E., Riquelme, F., & René Venegas, V. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6441-2_43
Accelerating results with no copy-paste hassle
Janokar, S., Gaikwad, V. D., Supe, J., Thakur, S., Shinde, S., & Malode, S. (2025) · AIP conference proceedings · doi:10.1063/5.0292340
The Importance and Place of Artificial Intelligence in Design
KAYIRAN, H. F. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1315-3.ch008
Information Granulation for Tasks of Assessing Malicious Activity in the Smart City Infrastructure
Kotenko, I. V., & Parashchuk, I. B. (2026) · Studies in systems, decision and control · doi:10.1007/978-3-032-02717-7_18
OpenITI Tabanlı RAG Sistemi ile Doğal Dil İşleme
Tay, M. (2026) · Akademisyen Kitabevi eBooks · doi:10.37609/akya.4033
A 40 nm Buffer-Free 7T-SRAM Analog Charge-Domain CIM Macro With Merging Timing Based On Time-Row Division Strategy
Jiang, L., Wu, J., Sun, S., Li, C., Kang, W., & Zhang, H. (2026) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2026.3656426
Verifying Robustness of Neural Networks with Tight Semidefinite Relaxations
Azuma, G., KIM, S., & Yamashita, M. (2026) · Computational Optimization and Applications · doi:10.1007/s10589-026-00765-5
Basic Text Analysis, Preprocessing, Bag-of-Words, TF-IDF, and Exploratory Statistics
Dan, D., & Reutterer, T. (2025) · Classroom companion: business · doi:10.1007/978-3-032-08086-8_4
Text Classification, LDA, KNN, SVM, Neural Networks, and FastText
Dan, D., & Reutterer, T. (2025) · Classroom companion: business · doi:10.1007/978-3-032-08086-8_6
Disentangling hierarchical and sequential computations during sentence processing
Zacharopoulos, C.-N., Dehaene, S., & Lakretz, Y. (2026) · Cortex · doi:10.1016/j.cortex.2026.02.004 · Cited by 3
PyMentor: Domain-Independent Intelligent Tutoring System Using Video-to-Text Processing and Retrieval-Augmented GenerationPreprint
Naik, V., & Jain, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6303484
A comparative approach on classification of medical condition drug reviews using multiple traditional and neural network-based models
Fu, H. (2026) · Computer Methods in Biomechanics & Biomedical Engineering · doi:10.1080/10255842.2026.2641195
“In order to update the weights, the backpropagation computes the error between the network’s anticipated output and the actual output (Goldberg 2017).”
PyMentor: Domain-Independent Intelligent Tutoring System Using Video-to-Text Processing and Retrieval-Augmented GenerationPreprint
Naik, V., & Jain, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6515846
Neural Methods for Sentiment Analysis and Text Summarization
Le, T.-H. (2020) · theses.fr (ABES) · doi:10.70675/e5daf161ze829z4e0fzbd75zb727195984b2
Emerging linguistic universals in communicating neural network agents
Chaabouni, R. (2021) · theses.fr (ABES) · doi:10.70675/bc536b46z9d4ez461bz8607zde5f498ca922
Deep Learning-Based Sentiment Analysis in English Literature
Santhi, P., Yamala, R. L., Pokala, R. S., Shaik, S., Ram, S., Raju, K. K., & Bolla, J. V. (2026) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi68868.2026.11466299
Emerging Trend Detection in News Articles
Nguyen, N. K. (2023) · theses.fr (ABES) · doi:10.70675/7a92cf20z7488z4551zb81dz255fc375df3b
51716 Sprachverarbeitung
Plank, B. (2026) · doi:10.1515/9783111386690-020
Automated identification of cancer-associated thrombosis events via natural language processing: a systematic review of the literature
Boyne, A., Zhou, E., & Li, A. (2026) · Bleeding Thrombosis and Vascular Biology · doi:10.4081/btvb.2026.437

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