Cited by 825Natural Language Processing TechniquesTopic ModelingSpeech Recognition and Synthesis

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Neural Network Methods for Natural Language Processing

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

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

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Deep Residual Learning for Image Recognition
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“It is known that window size affects semantic similarity: larger window sizes tend to capture broader, more topical similarities between words whilst smaller windows capture more linguistic or even syntactic similarities (Goldberg, 2017, Sec.”
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Deep Learning for Plasma Tomography and Disruption Prediction From Bolometer Data
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Futrell, R., Wilcox, E., Morita, T., & Lévy, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.01329 · Cited by 50
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“A RNN is a deep neural network model used for the analysis of sequential data in natural language processing (NLP) and speech recognition applications [27].”
Method
Multi-Task Recommendations with Reinforcement Learning
Liu, Z., Tian, J., Cai, Q., Zhao, X., Gao, J., Liu, S., Chen, D., He, T., Zheng, D., Jiang, P., et al. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583467 · Cited by 48
“and transferring information among tasks [5], which has been developed for a wide range of machine learning applications, including computer vision [38], natural language processing [14], clickthrough rate (CTR) and click-through&conversion rate (CTCVR) prediction [29].”
Background
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“RNN The RNN is a deep learning model for processing sequence data, such as stock charts [30], music [31], and natural language processes [32].”
Method
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Background
Survey of Neural Text Representation Models
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“…of language features, elimination of the need for manual features engineering, the ability to model complex relationships that may elude traditional ML algorithms, and facilitating a deeper understanding of intricate and subtle changes in speech and language (Goldberg, 2017; Young et al., 2018).”
BackgroundMethod
Proceedings of the First Workshop on Gender Bias in Natural Language Processing
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Meaningful variable names for decompiled code
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Contextual word representations
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“Although they have been mainly used or natural language processing ( Goldberg, 2017 ), they have also been pplied in other fields, such as image generation ( Gregor et al., 2015 ) nd AD progression and prediction ( Lee et al., 2019; Mehdipour Ghazi t al., 2019; Nguyen et al., 2018; Wang et al.,…”
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“Readers who lack this background should consult introductory machine learning and DL textbooks ﬁrst (Murphy, 2012; Goodfellow et al., 2016; Goldberg, 2017; Charniak, 2018).”
Background
An Empirical Assessment of Machine Learning Approaches for Triaging Reports of a Java Static Analysis Tool
Koç, U., Wei, S., Foster, J. S., Carpuat, M. J., & Porter, A. (2019) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst.2019.00036 · Cited by 36
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Naturally occurring language as a source of evidence in suicide prevention
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Frequency, acceptability, and selection: A case study of clause-embedding
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Deep-learning-based reading eye-movement analysis for aiding biometric recognition
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“Goldberg [11] provides an efficient approach to outperform traditional rule-based, dictionary-based, and machine learning models by supporting multilayering, non-linear activation functions, and capable of capturing long-term dependencies.”
Method
Capturing a News Frame – Comparing Machine-Learning Approaches to Frame Analysis with Different Degrees of Supervision
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“Additionally, we implemented a Multi Layer Perceptron (Goldberg, 2017) as a neural network approach, proven to be effective for such classification tasks (F. Lind et al., 2021).”
Method
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Automatic Classification of Text Complexity
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A Systematic Study of Leveraging Subword Information for Learning Word Representations
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#DontTweetThis: Scoring Private Information in Social Networks
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Using textual analysis to identify merger participants: Evidence from the U.S. banking industry
Katsafados, A. G., Androutsopoulos, I., Chalkidis, I., Fergadiotis, E., Leledakis, G. N., & Pyrgiotakis, E. G. (2021) · Finance research letters · doi:10.1016/j.frl.2021.101949 · Cited by 30
STCNet: spatiotemporal cross network for industrial smoke detection
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Application of Deep Learning for Quality of Service Enhancement in Internet of Things: A Review
Kimbugwe, N., Pei, T., & Kyebambe, M. N. (2021) · Energies · doi:10.3390/en14196384 · Cited by 32
Semi-Automated evidence synthesis in health psychology: current methods and future prospects
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Evaluation of text representation schemes and distance measures for authorship linking
Kocher, M., & Savoy, J. (2018) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqy013 · Cited by 28
ACTUARIAL APPLICATIONS OF WORD EMBEDDING MODELS
Lee, G. Y., Manski, S., & Maiti, T. (2019) · Astin Bulletin · doi:10.1017/asb.2019.28 · Cited by 28
Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews
Aldabbas, H., Bajahzar, A., Alruily, M., Qureshi, A. A., Latif, R. M. A., & Farhan, M. (2020) · Journal of Intelligent Systems · doi:10.1515/jisys-2019-0197 · Cited by 28
A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis
Ke, W., & Chan, K. (2021) · Applied Sciences · doi:10.3390/app112311344 · Cited by 29
Is deep learning better than kernel regression for functional connectivity prediction of fluid intelligence?
He, T., Kong, R., Holmes, A. J., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2018) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2018.8423958 · Cited by 30
“Deep neural networks (DNN) have been used to achieve many state-of-the-art results in multiple areas in machine learning [3], [4].”
Method
Confirm or refute?: A comparative study on citation sentiment classification in clinical research publications
Halil Kilicoglu, B., Peng, Z., Tafreshi, S., Tran, T., Rosemblat, G., & Schneider, J. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103123 · Cited by 28
A novel method to detect cyber-attacks in IoT/IIoT devices on the modbus protocol using deep learning
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Using AI and NLP for Tacit Knowledge Conversion in Knowledge Management Systems: A Comparative Analysis
Seghroucheni, O. Z., Lazaar, M., & Achhab, M. A. (2025) · Technologies · doi:10.3390/technologies13020087 · Cited by 39
“The study of NLP requires not only a deep understanding of how language works but also the computational power to handle vast amounts of data and complex models, making it an interdisciplinary field at the forefront of AI research [5,6].”
Information Retrieval: Concepts, Models, and Systems
Gudivada, V. N., Rao, D. L., & Gudivada, A. R. (2018) · Handbook of statistics · doi:10.1016/bs.host.2018.07.009 · Cited by 27
A novel method for speed training acceleration of recurrent neural networks
Bilski, J., Rutkowski, L., Smoląg, J., & Tao, D. (2020) · Information Sciences · doi:10.1016/j.ins.2020.10.025 · Cited by 27
Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement
Qiao, X., Zhang, Z., & Chen, X. (2020) · Applied Sciences · doi:10.3390/app10217430 · Cited by 27
“Neural networks are the most widely used type of deep learning model, which exhibit excellent performance in solving complex nonlinear regression problems [16,17].”
Background
The IBM natural conversation framework: a new paradigm for conversational UX design
Moore, R. J., An, S., & Ren, G.-J. (2023) · Human–Computer Interaction · doi:10.1080/07370024.2022.2081571 · Cited by 26
Effort Estimation via Text Classification And Autoencoders
Soares, R. G. F. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489030 · Cited by 26
Detecting Cyber Threats in Non-English Dark Net Markets: A Cross-Lingual Transfer Learning Approach
Ebrahimi, M. R., Surdeanu, M., Samtani, S., & Chen, H. (2018) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi.2018.8587404 · Cited by 26
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“Early approaches used maximum marginal relevance to add items to the list [5], while recent works have adopted deep Neural Networks (NN) [6, 16].”
Method
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“Furthermore, we also explore the option of using popular word embeddings to represent text (Goodfellow et al., 2016; Goldberg, 2017).”
BackgroundMethod
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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

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