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

Detection of Distributed Denial of Charge (DDoC) Attacks Using Deep Neural Networks With Vector Embedding

Shafee, A. A., Mahmoud, M. M. E. A., Srivastava, G., Fouda, M. M., Alsabaan, M., & Ibrahem, M. I. (2023) · IEEE Access · doi:10.1109/access.2023.3296562 · Cited by 23

Quantum Natural Language Processing: A Comprehensive Survey

Moudina, C. V., Fendji, J. L. E. K., Schöning, J., & Atemkeng, M. (2024) · IEEE Access · doi:10.1109/access.2024.3420707 · Cited by 25

Characterization of Synthetic Health Data Using Rule-Based Artificial Intelligence Models

Lenatti, M., Paglialonga, A., Orani, V., Ferretti, M., & Mongelli, M. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3236722 · Cited by 24

An Iterative Polishing Framework Based on Quality Aware Masked Language Model for Chinese Poetry Generation

Deng, L., Wang, J., Liang, H., Chen, H., Xie, Z., Zhuang, B., Wang, S., & Xiao, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6265 · Cited by 25

A short survey on end-to-end simple question answering systems

Franco, W., Venceslau, A. D. P., Sales, J. E., Maia, J. G. R., Pinheiro, V. C. M., & Vidal, V. M. P. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09826-5 · Cited by 26

Chatbot Design and Implementation: Towards an Operational Model for Chatbots

Skuridin, A., & Wynn, M. G. (2024) · Information · doi:10.3390/info15040226 · Cited by 33

A Study of fastText Word Embedding Effects in Document Classification in Bangla Language

Mojumder, P., Hasan, M. M., Hossain, M. F., & Hasan, K. M. A. (2020) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-52856-0_35 · Cited by 22

kDeepBind: Prediction of RNA-Proteins binding sites using convolution neural network and k-gram features

Tahir, M., Tayara, H., Hayat, M., & Chong, K. T. (2020) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2020.104217 · Cited by 22

Integrating Speculation Detection and Deep Learning to Extract Lung Cancer Diagnosis from Clinical Notes

Pabón, O. S., Torrente, M., Provencio, M., Rodríguez‐González, A., & Menasalvas, E. (2021) · Applied Sciences · doi:10.3390/app11020865 · Cited by 22

Deep Learning for Toponym Resolution: Geocoding Based on Pairs of Toponyms

Fize, J., Moncla, L., & Martins, B. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10120818 · Cited by 22

BioRead: A New Dataset for Biomedical Reading Comprehension

Pappas, D., Androutsopoulos, I., & Papageorgiou, H. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2k6jps9hh3ui · Cited by 22

Predicting and interpreting embeddings for out of vocabulary words in downstream tasks

Garneau, N., Leboeuf, J.-S., & Lamontagne, L. (2018) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w18-5439 · Cited by 22

Do RNNs learn human-like abstract word order preferences?Preprint

Futrell, R., & Lévy, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.01866 · Cited by 22

Contextual Word Representations: A Contextual IntroductionPreprint

Smith, N. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.06006 · Cited by 41

Hand-drawn sketch recognition with a double-channel convolutional neural network

Zhang, L. (2021) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-021-00752-4 · Cited by 21

A Novel Deep Learning Approach Using Contextual Embeddings for Toponym Resolution

Cardoso, A. B., Martins, B., & Estima, J. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11010028 · Cited by 22

Large Language Models Trained on Equipment Maintenance Text

Abijith, P., Patidar, P., Nair, G., & Pandya, R. (2023) · Abu Dhabi International Petroleum Exhibition & Conference (ADIPEC) · doi:10.2118/216336-ms · Cited by 23

Contextual Fine-Tuning of Language Models with Classifier-Driven Content Moderation for Text Generation

Matan, P., & Velvizhy, P. (2024) · Entropy · doi:10.3390/e26121114 · Cited by 21

Improved Mahalanobis Distance Based JITL-LSTM Soft Sensor for Multiphase Batch Processes

Zheng, J., Shen, F., & Ye, L. (2021) · IEEE Access · doi:10.1109/access.2021.3079184 · Cited by 21

Subregular Complexity and Deep LearningPreprint

Avcu, E., Shibata, C., & Heinz, J. N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.05940 · Cited by 26

Phonological theory and computational modelling

Chandlee, J., & Jardine, A. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198796800.003.0031 · Cited by 20

Distributed language representation for authorship attribution

Kocher, M., & Savoy, J. (2017) · Digital Scholarship in the Humanities · doi:10.1093/llc/fqx046 · Cited by 21

Grammatical Error Checking Systems: A Review of Approaches and Emerging Directions

Madi, N., & Al-Khalifa, H. S. (2018) · International Conference on Digital Information Management · doi:10.1109/icdim.2018.8847020 · Cited by 20

Error Detection for Arabic Text Using Neural Sequence Labeling

Madi, N., & Al-Khalifa, H. S. (2020) · Applied Sciences · doi:10.3390/app10155279 · Cited by 23

Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives

Alam, T., Al-Absi, H. R. H., & Schmeier, S. (2020) · Non-Coding RNA · doi:10.3390/ncrna6040047 · Cited by 21

Information Theory as a Bridge Between Language Function and Language Form

Futrell, R., & Hahn, M. (2022) · Frontiers in Communication · doi:10.3389/fcomm.2022.657725 · Cited by 21

A duplication-free quantum neural network for universal approximation

Hou, X., Zhou, G., Li, Q., Jin, S., & Wang, X. (2023) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-023-2098-8 · Cited by 23

Explaining Simple Natural Language Inference

Kalouli, A.-L., Buis, A., Real, L., Palmer, M. S., & de Paiva, V. (2019) · Linguistic Annotation Workshop · doi:10.18653/v1/w19-4016 · Cited by 20

Stacked authorship attribution of digital texts

Custódio, J. E., & Paraboni, I. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.114866 · Cited by 21

Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized EmbeddingsPreprint

Sahrawat, D., Mahata, D., Kulkarni, M., Zhang, H., Gosangi, R., Stent, A. J., Sharma, A., Kumar, Y., Shah, R. R., & Zimmermann, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.08840 · Cited by 30

Large language models driven BIM-based DfMA method for free-form prefabricated buildings: framework and a usefulness case study

Han, D., Zhao, W., Yin, H., Qu, M., Zhu, J., Ma, F., Ying, Y., & Pan, A. (2025) · Journal of Asian Architecture and Building Engineering · doi:10.1080/13467581.2024.2329351 · Cited by 20

On the Use of Parsing for Named Entity Recognition

Alonso, M. Á., Gómez‐Rodríguez, C., & Vilares, J. (2021) · Applied Sciences · doi:10.3390/app11031090 · Cited by 23

Transmission dynamics informed neural network with application to COVID-19 infections

He, M., Tang, B., Xiao, Y., & Tang, S. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107431 · Cited by 18

Prognostic models of in-hospital mortality of intensive care patients using neural representation of unstructured text: A systematic review and critical appraisal

Vagliano, I., Dormosh, N., Rios, M., Luik, T. T., Buonocore, T. M., Elbers, P., Dongelmans, D. A., Schut, M. C., & Abu–Hanna, A. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104504 · Cited by 22

A Richer-but-Smarter Shortest Dependency Path with Attentive Augmentation for Relation Extraction

Can, D.-C., Le, H.-Q., Ha, Q.-T., & Collier, N. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1298 · Cited by 21

Word-order Biases in Deep-agent Emergent Communication

Chaabouni, R., Kharitonov, E., Lazaric, A., Emmanuel Dupoux, A., & Baroni, M. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1509 · Cited by 31

Word Embedding Evaluation for Sinhala

Lakmal, D., Ranathunga, S., Peramuna, S., & Herath, I. (2020) · Language Resources and Evaluation · Cited by 19

Social Robot Interactions for Social Engineering: Opportunities and Open Issues

Abate, A. F., Bisogni, C., Cascone, L., Castiglione, A., Costabile, G., & Mercuri, I. (2020) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00097 · Cited by 19

Text structuring methods based on complex network: a systematic review

Oliva, S. Z., Oliveira-Ciabati, L., Dezembro, D. G., Júnior, M. S. A., Silva, M. D. C., Pessotti, H. C., & Pollettini, J. T. (2021) · Scientometrics · doi:10.1007/s11192-020-03785-y · Cited by 19

Character-based Neural Semantic Parsing

van Noord, R. (2021) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.169308968 · Cited by 18

Analysis of Italian Word Embeddings

Tripodi, R., & Pira, S. L. (2017) · Accademia University Press eBooks · doi:10.4000/books.aaccademia.2475 · Cited by 18

Sentiment analysis in Turkish: Supervised, semi-supervised, and unsupervised techniques

Aydın, C. R., & Güngör, T. (2020) · Natural Language Engineering · doi:10.1017/s1351324920000200 · Cited by 19

Deep Neural Network Framework Based on Word Embedding for Protein Glutarylation Sites Prediction

Liu, C.-M., Ta, V.-D., Le, N. Q. K., Addis, D., & Shi, C. (2022) · Life · doi:10.3390/life12081213 · Cited by 18

Bayesian bilinear neural network for predicting the mid‐price dynamics in limit‐order book markets

Magris, M., Shabani, M., & Iosifidis, A. (2023) · Journal of Forecasting · doi:10.1002/for.2955 · Cited by 19

SPARQL-QA-v2 system for Knowledge Base Question Answering

Borroto, M., & Ricca, F. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120383 · Cited by 20

Computational humor recognition: a systematic literature review

Kalloniatis, A., & Adamidis, P. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-11043-3 · Cited by 18

An Ensemble Approach to Cross-Domain Authorship Attribution

Custódio, J. E., & Paraboni, I. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28577-7_17 · Cited by 18

A Taxonomy for Deep Learning in Natural Language Processing

Landolt, S., Wambsganss, T., & Söllner, M. (2021) · Hawaii International Conference on System Sciences · doi:10.24251/hicss.2021.129 · Cited by 18

The effectiveness of machine score-ability ratings in predicting automated scoring performance

Lottridge, S. M., Wood, S., & Shaw, D. (2018) · Applied Measurement in Education · doi:10.1080/08957347.2018.1464452 · Cited by 18

A critical deconstruction of computer-based test application in Turkish State University

Ulum, Ö. G. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10199-z · Cited by 17

LSTMEmbed: Learning Word and Sense Representations from a Large Semantically Annotated Corpus with Long Short-Term Memories

Iacobacci, I., & Roberto Navigli, A. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1165 · Cited by 18

Learning Syntactic Dense Embedding with Correlation Graph for Automatic Readability Assessment

Qiu, X., Chen, Y., Chen, H., Nie, J., Shen, Y., & Lü, D. (2021) · doi:10.18653/v1/2021.acl-long.235 · Cited by 18

There’s So Much to Do and Not Enough Time to Do It! A Case for Sentiment Analysis to Derive Meaning From Open Text Using Student Reflections of Engineering Activities

Roy, A. R., & Rambo-Hernandez, K. E. (2021) · American Journal of Evaluation · doi:10.1177/1098214020962576 · Cited by 17

Model scheduling and sample selection for ensemble adversarial example attacks

Hu, Z., Li, H., Yuan, L., Cheng, Z., Yuan, W. M., & Zhu, M. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108824 · Cited by 19

Word embeddings for negation detection in health records written in Spanish

Santiso, S., Casillas, A., Pérez, A., & Oronoz, M. (2018) · Soft Computing · doi:10.1007/s00500-018-3650-7 · Cited by 17

Deep recurrent neural network for geographical entities disambiguation on social media data

Zuheros, C., Tabik, S., Valdivia, A., Martínez‐Cámara, E., & Herrera, F. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.02.030 · Cited by 17

A computationally efficient BiLSTM based approach for the binary sentiment classification

Hameed, Z., García-Zapirain, B., & Ruiz, I. (2019) · IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) · doi:10.1109/isspit47144.2019.9001781 · Cited by 18

Suggesting Comment Completions for Python using Neural Language Models

Ciurumelea, A., Proksch, S., & Gall, H. C. (2020) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner48275.2020.9054866 · Cited by 18

Scope of Sentiment Analysis on News Articles Regarding Stock Market and GDP in Struggling Economic Condition

Biswas, S. (2020) · International Journal of Emerging Trends in Engineering Research · doi:10.30534/ijeter/2020/117872020 · Cited by 17

Proceedings of the 13th Linguistic Annotation Workshop

Friedrich, A., Zeyrek, D. D., & Hoek, J. (2019) · doi:10.18653/v1/w19-40 · Cited by 17

Extracting Cause of Death from Verbal Autopsy with Deep Learning interpretable methods

Blanco, A., Perez, A., Casillas, A., & Cobos, D. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3005769 · Cited by 17

Toward a Psychology of Deep Reinforcement Learning Agents Using a Cognitive Architecture

Mitsopoulos, K., Somers, S., Schooler, J. N., Lebiere, C. J., Pirolli, P. L. T., & Thomson, R. (2021) · Topics in Cognitive Science · doi:10.1111/tops.12573 · Cited by 17

The use of sentiment and emotion analysis and data science to assess the language of nutrition-, food- and cooking-related content on social media: a systematic scoping review

Molenaar, A., Jenkins, E. L., Brennan, L. L., Lukose, D., & McCaffrey, T. A. (2023) · Nutrition Research Reviews · doi:10.1017/s0954422423000069 · Cited by 20

Generating, computing and recognizing with virus machines

Ramírez-de-Arellano, A., Orellana-Martín, D., & Pérez-Jímenez, M. J. (2023) · Theoretical Computer Science · doi:10.1016/j.tcs.2023.114077 · Cited by 18

Establishing Trustworthiness Through Algorithmic Approaches to Qualitative Research

Nguyen, H., Ahn, J., Belgrave, A. B., Lee, J., Cawelti, L., Kim, H. E., Prado, Y., Santagata, R., & Villavicencio, A. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-67788-6_4 · Cited by 43

Data Mining for Cyberbullying and Harassment Detection in Arabic Texts

Bashir, E., & Bouguessa, M. (2021) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2021.05.04 · Cited by 16

Deep cascaded multitask framework for detection of temporal orientation, sentiment and emotion from suicide notes

Ghosh, S. K., Ekbal, A., & Bhattacharyya, P. (2022) · Scientific Reports · doi:10.1038/s41598-022-08438-z · Cited by 16

Machine learning for learner English

Ballier, N., Canu, S., Petitjean, C., Gasso, G., Balhana, C., Alexopoulou, T., & Gaillat, T. (2020) · International Journal of Learner Corpus Research · doi:10.1075/ijlcr.18012.bal · Cited by 16

Automatic Arabic Dialect Identification Systems for Written Texts: A SurveyPreprint

Althobaiti, M. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.12622 · Cited by 16

Similarity-based approach for inventive design solutions assistance

Ni, X., Samet, A., & Cavallucci, D. (2021) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-021-01749-4 · Cited by 16

Predicting input validation vulnerabilities based on minimal SSA features and machine learning

Marashdih, A. W., Zaaba, Z. F., & Suwais, K. M. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.09.010 · Cited by 15

Task-Oriented ML/DL Library Recommendation Based on a Knowledge Graph

Liu, M., Zhao, C., Peng, X., Yu, S. S., Wang, H., & Sha, C. (2023) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3285280 · Cited by 16

A study of Turkish emotion classification with pretrained language models

Uçan, A., Dörterler, M., & Sezer, E. A. (2021) · Journal of Information Science · doi:10.1177/0165551520985507 · Cited by 15

Machine Learning approach for Credit ScoringPreprint

Provenzano, A. R., Trifirò, D., Datteo, A., Giada, L., Jean, N., Riciputi, A., Pera, G. L., Spadaccino, M., Massaron, L., & Nordio, C. (2020) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.2008.01687 · Cited by 15

RADAR#: An Ensemble Approach for Radicalization Detection in Arabic Social Media Using Hybrid Deep Learning and Transformer Models

Al-Shawakfa, E. M., Alsobeh, A., Omari, S., & Shatnawi, A. (2025) · Information · doi:10.3390/info16070522 · Cited by 18

Visual Search Target Inference Using Bag of Deep Visual Words

Stauden, S., Barz, M., & Sonntag, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00111-7_25 · Cited by 14

Viability of Neural Networks for Core Technologies for Resource-Scarce Languages

Loubser, M., & Puttkammer, M. J. (2020) · Information · doi:10.3390/info11010041 · Cited by 14

Detailed Study of Deep Learning Models for Natural Language Processing

Gupta, M., Verma, S. K., & Jain, P. (2020) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icacccn51052.2020.9362989 · Cited by 14

Machine-learned prediction of the electronic fields in a crystal

Teh, Y. S., Ghosh, S., & Bhattacharya, K. (2021) · Mechanics of Materials · doi:10.1016/j.mechmat.2021.104070 · Cited by 14

Likes on image posts in social networking services: Impact of travel episode

Tamaki, S. (2021) · Journal of Destination Marketing & Management · doi:10.1016/j.jdmm.2021.100615 · Cited by 16

Machine Learning Approach for Personality Recognition in Spanish Texts

Hernández, Y., Martínez, A., Estrada, H., Ortiz-Hernández, J., & Acevedo, C. M. (2022) · Applied Sciences · doi:10.3390/app12062985 · Cited by 14

ADDI: Recommending alternatives for drug–drug interactions with negative health effects

Allahgholi, M., Rahmani, H., Javdani, D., Weiß, G., & Módos, D. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103969 · Cited by 14

Verification of Recurrent Neural Networks with Star Reachability

Tran, H.-D., Choi, S.-W., Yang, X.-D., Yamaguchi, T., Hoxha, B., & Prokhorov, D. (2023) · International Conference on Hybrid Systems: Computation and Control · doi:10.1145/3575870.3587128 · Cited by 19

Computer-Aided Optimisation in Additive Manufacturing Processes: A State of the Art Survey

Henriksen, T. E., Brustad, T. F., Dalmo, R., & Pedersen, A. (2024) · Journal of Manufacturing and Materials Processing · doi:10.3390/jmmp8020076 · Cited by 13

A Deep Learning Approach for Sentiment Analysis Applied to Hotel’s Reviews

de Souza, J. G. R., Oliveira, A. D. P., de Andrade, G. C., & Moreira, A. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91947-8_5 · Cited by 13

Nakdan: Professional Hebrew Diacritizer

Shmidman, A., Shmidman, S., Koppel, M., & Goldberg, Y. (2020) · Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL Demo) · doi:10.18653/v1/2020.acl-demos.23 · Cited by 13

Grammatical structure detection by Instinct Plasticity based Echo State Networks with Genetic Algorithm

Liu, Z., Li, S., Pan, M., & Loo, C. K. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.09.073 · Cited by 13

Understanding the Source of Semantic Regularities in Word Embeddings

Chiang, H.-Y., Camacho-Collados, J., & Pardos, Z. A. (2020) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2020.conll-1.9 · Cited by 13

Detecting Cyber Threats in Non-English Hacker Forums: An Adversarial Cross-Lingual Knowledge Transfer Approach

Ebrahimi, M. R., Samtani, S., Chai, Y., & Chen, H. (2020) · IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw50608.2020.00021 · Cited by 13

Phonetic explanation in phonology

Kingston, J. K. C. (2022) · Oxford University Press eBooks · doi:10.1093/oso/9780198796800.003.0028 · Cited by 14

A survey of automatic de-identification of longitudinal clinical narrativesPreprint

Yogarajan, V., Mayo, M., & Pfahringer, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.06765 · Cited by 13

An empirical assessment of machine learning approaches for triaging reports of static analysis tools

Yerramreddy, S., Mordahl, A., Koç, U., Wei, S., Foster, J. S., Carpuat, M. J., & Porter, A. (2023) · Empirical Software Engineering · doi:10.1007/s10664-022-10253-z · Cited by 14

Extracting interpretable features for time series analysis: A Bag-of-Functions approach

Klopries, H., & Schwung, A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119787 · Cited by 12

Context-aware citation recommendation of scientific papers: comparative study, gaps and trends

Jebari, C., Herrera‐Viedma, E., & Cobo, M. J. (2023) · Scientometrics · doi:10.1007/s11192-023-04773-8 · Cited by 16

Ranking Models in Unlabeled New Environments

Sun, X., Hou, Y., Deng, W., Li, H., & Zheng, L. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01155 · Cited by 12

Quality Prediction in Directed Energy Deposition Using Artificial Neural Networks Based on Process Signals

Marko, A., Bähring, S., Raute, J., Biegler, M., & Rethmeier, M. (2022) · Applied Sciences · doi:10.3390/app12083955 · Cited by 12

Towards Learning Generalizable Code Embeddings Using Task-agnostic Graph Convolutional Networks

Ding, Z., Li, H., Shang, W., & Chen, T.-H. P. (2022) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3542944 · Cited by 13

Machine learning classification of non-Markovian noise disturbing quantum dynamics

Martina, S., Gherardini, S., & Caruso, F. (2023) · Physica Scripta · doi:10.1088/1402-4896/acb39b · Cited by 25

COVID-19 Detection from Cough Recordings Using Bag-of-Words Classifiers

Pavel, I., & Ciocoiu, I. B. (2023) · Sensors · doi:10.3390/s23114996 · Cited by 12

Applying Artificial Intelligence to The Patent System

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