Rationale-Augmented Convolutional Neural Networks for Text Classification

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

The Cochrane Collaboration's tool for assessing risk of bias in randomised trials

Higgins, J. P. T., Altman, D. G., Gotzsche, P. C., Juni, P., Moher, D., Oxman, A. D., Savovic, J., Schulz, K. F., Weeks, L., Sterne, J. A. C., et al. (2011) · BMJ · doi:10.1136/bmj.d5928 · Cited by 33,817

Untitled work

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The “Golden Age” of Probiotics: A Systematic Review and Meta-Analysis of Randomized and Observational Studies in Preterm Infants

Dermyshi, E., Wang, Y., Yan, C., Hong, W., Qiu, G., Gong, X., & Zhang, T. (2017) · Neonatology · doi:10.1159/000454668 · Cited by 24,586

Neural Machine Translation by Jointly Learning to Align and Translate

Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213

Convolutional Neural Networks for Sentence Classification

Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831

Text categorization with Support Vector Machines: Learning with many relevant features

Joachims, T. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026683 · Cited by 8,271

Distributed Representations of Sentences and DocumentsPreprint

Le, Q. V., & Mikolov, T. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1405.4053 · Cited by 7,905

ADADELTA: An Adaptive Learning Rate Method

Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322

A unified architecture for natural language processing: deep neural networks with multitask learning

Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058

Recurrent convolutional neural kernel model for stock price movement prediction

Liu, S., Zhang, X., Wang, Y., & Feng, G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0234206 · Cited by 23

The risk of racial bias while tracking influenza-related content on social media using machine learning

Lwowski, B., & Rios, A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa326 · Cited by 23

Spatiotemporal trajectories in resting-state FMRI revealed by convolutional variational autoencoder

Zhang, X., Maltbie, E. A., & Keilholz, S. D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118588 · Cited by 22

Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillation

Tian, Y., Han, Y., Chen, X., Wang, W. W., & Chawla, N. V. (2025) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3701551.3703577 · Cited by 63

Jumper: Learning When to Make Classification Decision in Reading

Liu, X., Mou, L., Cui, H., Lu, Z., & Song, S. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/589 · Cited by 24

Using case-level context to classify cancer pathology reports

Gao, S. B., Alawad, M., Schaefferkoetter, N., Penberthy, L. T., Wu, X., Durbin, E. B., Coyle, L. M., Ramanathan, A., & Tourassi, G. D. (2020) · PLoS ONE · doi:10.1371/journal.pone.0232840 · Cited by 22

Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study

Jin, Y., Li, F., Vimalananda, V. G., & Yu, H. Q. (2019) · JMIR Medical Informatics · doi:10.2196/14340 · Cited by 22

Wave based damage detection in solid structures using spatially asymmetric encoder–decoder network

Wuttke, F., Lyu, H., Sattari, A. S., & Rizvi, Z. H. (2021) · Scientific Reports · doi:10.1038/s41598-021-00326-2 · Cited by 24

Optimizing News Categorization with Machine Learning: A Comprehensive Study Using Naive Bayes (MultinomialNB) Classifier

Mansoori, A. S., Tahat, K. M., Tahat, D. N., Habes, M., & Salloum, S. A. (2024) · Studies in big data · doi:10.1007/978-3-031-70855-8_15 · Cited by 21

An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models

Li, F., & Yu, H. Q. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz018 · Cited by 21

Opinion mining in Persian language using a hybrid feature extraction approach based on convolutional neural network

Zobeidi, S., Naderan, M., & Alavi, S. E. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07993-4 · Cited by 22

Development and evaluation of novel ophthalmology domain-specific neural word embeddings to predict visual prognosis

Wang, S. Y., Tseng, B., & Hernandez‐Boussard, T. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104464 · Cited by 20

Using word embeddings to improve the privacy of clinical notes

Abdalla, M., Abdalla, M., Rudzicz, F., & Hirst, G. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa038 · Cited by 20

A Keyword-Enhanced Approach to Handle Class Imbalance in Clinical Text Classification

Blanchard, A. E., Gao, S. B., Yoon, H., Christian, J. B., Durbin, E. B., Wu, X., Stroup, A. M., Doherty, J. A., Schwartz, S. M., Wiggins, C. L., et al. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3141976 · Cited by 19

Automating Quality Assessment of Medical Evidence in Systematic Reviews: Model Development and Validation Study

Šuster, S., Baldwin, T., Lau, J. H., Yepes, A. J., Martínez, D., Otmakhova, Y., & Verspoor, K. M. (2023) · Journal of Medical Internet Research · doi:10.2196/35568 · Cited by 19

Combining Unsupervised Pre-training and Annotator Rationales to Improve Low-shot Text Classification

Melamud, O., Bornea, M. A., & Barker, K. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1401 · Cited by 19

Human Rationales as Attribution Priors for Explainable Stance Detection

Jayaram, S., & Allaway, E. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.450 · Cited by 19

TPepPro: a deep learning model for predicting peptide–protein interactions

Xiao-hong, J., Chen, Z., Yu, D., Jiang, Q., Chen, Z., Yan, B., Qin, J., Liu, Y., & Wang, J. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae708 · Cited by 18

Interpretable Charge Prediction for Criminal Cases with Dynamic Rationale Attention

Chao, W., Jiang, X., Luo, Z., Hu, Y., & Ma, W. (2019) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11377 · Cited by 17

A survey on improving NLP models with human explanations

Hartmann, M., & Sonntag, D. (2022) · doi:10.18653/v1/2022.lnls-1.5 · Cited by 24

Application of BERT to Enable Gene Classification Based on Clinical Evidence

Su, Y., Xiang, H., Xie, H., Yu, Y., Dong, S., Yang, Z., & Zhao, N. (2020) · BioMed Research International · doi:10.1155/2020/5491963 · Cited by 17

Less is More: Attention Supervision with Counterfactuals for Text Classification

Choi, S. L., Park, H., Yeo, J., & Hwang, S.-W. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.543 · Cited by 16

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Hsieh, C.-Y., Li, C.-L., Yeh, C.-K., Nakhost, H., Fujii, Y., Ratner, A., Krishna, R., Lee, C.-Y., & Pfister, T. (2023) · arXiv · doi:10.48550/arxiv.2305.02301 · Cited by 16

Identifying the Risk Factors of Allergic Rhinitis Based on Zhihu Comment Data Using a Topic-Enhanced Word-Embedding Model: Mixed Method Study and Cluster Analysis

Gu, D., Wang, Q., Chai, Y., Yang, X., Zhao, W., Li, M., Золотарев, О., Xu, Z., & Zhang, G. (2024) · Journal of Medical Internet Research · doi:10.2196/48324 · Cited by 15

Learning credible DNNs via incorporating prior knowledge and model local explanation

Du, M., Liu, N., Yang, F., & Hu, X. (2020) · Knowledge and Information Systems · doi:10.1007/s10115-020-01517-5 · Cited by 17

Projection Word Embedding Model With Hybrid Sampling Training for Classifying ICD-10-CM Codes: Longitudinal Observational Study

Lin, C. S., Lou, Y.-S., Tsai, D., Lee, C., Hsu, C.-J., Wu, D.-C., Wang, M.-C., & Fang, W. (2019) · JMIR Medical Informatics · doi:10.2196/14499 · Cited by 14

Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization

Zhang, Y., Lease, M., & Wallace, B. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2024 · Cited by 16

Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis

Ramírez‐Cifuentes, D., Freire, A., Baeza-Yates, R. A., Sanz, N., Pedrero, A. Á., González-Rodríguez, A., Rochel, M. L., Vives, R. L., Velazquez, D. A., Gonfaus, J. M., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/25925 · Cited by 15

Diagnostics-Guided Explanation Generation

Atanasova, P., Simonsen, J. G., Lioma, C., & Augenstein, I. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21287 · Cited by 15

ExASAG: Explainable Framework for Automatic Short Answer Grading

Tornqvist, M., Mahamud, M., Guzman, E. M., & Farazouli, A. (2023) · Workshop on Innovative Use of NLP for Building Educational Applications · doi:10.18653/v1/2023.bea-1.29 · Cited by 15

Rissanen Data Analysis: Examining Dataset Characteristics via Description LengthPreprint

Perez, E., Kiela, D., & Cho, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03872 · Cited by 13

Analysis of predictive performance and reliability of classifiers for quality assessment of medical evidence revealed important variation by medical area

Šuster, S., Baldwin, T., & Verspoor, K. M. (2023) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2023.04.006 · Cited by 15

An Improved Double Channel Long Short-Term Memory Model for Medical Text Classification

Liang, S., Chen, X., Ma, J., Du, W., & Ma, H. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/6664893 · Cited by 13

News Text Mining-Based Business Sentiment Analysis and Its Significance in Economy

Yang, M., Jiang, B., Wang, Y., Hao, T., & Liu, Y. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.918447 · Cited by 13

Exploratory Analysis of Marketing and Non-marketing E-cigarette Themes on Twitter

Han, S., & Kavuluru, R. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47874-6_22 · Cited by 13

A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation.

Singh, G., Marshall, I. J., Thomas, J., Shawe-Taylor, J., & Wallace, B. C. (2017) · Proceedings of the 2017 ACM on Conference on Information and Knowledge Management · doi:10.1145/3132847.3132989 · Cited by 11

MuTCELM: An optimal multi-TextCNN-based ensemble learning for text classification

Agbesi, V. K., Chen, W., Yussif, S. B., Ukwuoma, C. C., Gu, Y. H., & Al–antari, M. A. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38515 · Cited by 12

Biomedical document triage using a hierarchical attention-based capsule network

Wang, J., Li, M., Diao, Q., Lin, H., Yang, Z., & Zhang, Y. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03673-5 · Cited by 11

Text Semantic Classification of Long Discourses Based on Neural Networks with Improved Focal Loss

Jiang, D., & He, J. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/8845362 · Cited by 13

An analysis of unconscious gender bias in academic texts by means of a decision algorithm

Orgeira-Crespo, P., Míguez‐Álvarez, C., Alonso, M. C., & Rivo-López, E. (2021) · PLoS ONE · doi:10.1371/journal.pone.0257903 · Cited by 12

What to Learn, and How: Toward Effective Learning from Rationales

Carton, S., Kanoria, S., & Tan, C. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.86 · Cited by 20

Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

Li, D., Hu, B., Chen, Q., Xu, T., Tao, J., & Zhang, Y. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21342 · Cited by 13

Looking for low vision: Predicting visual prognosis by fusing structured and free-text data from electronic health records

Gui, H., Tseng, B., Hu, W., & Wang, S. Y. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104678 · Cited by 10

Explainable Text Classification Techniques in Legal Document Review: Locating Rationales without Using Human Annotated Training Text Snippets

Mahoney, C. J., Gronvall, P., Huber-Fliflet, N., & Zhang, J. (2022) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata55660.2022.10020626 · Cited by 11

Hierarchical bi-directional attention-based RNNs for supporting document classification on protein–protein interactions affected by genetic mutations

Fergadis, A., Baziotis, C., Pappas, D., Papageorgiou, H., & Potamianos, A. (2018) · Database · doi:10.1093/database/bay076 · Cited by 10

Research on the detection model of mental illness of online forum users based on convolutional network

Guo, Y., Zhang, Z., & Xu, X. (2023) · BMC Psychology · doi:10.1186/s40359-023-01460-4 · Cited by 9

Extraction of entity relations from Chinese medical literature based on multi-scale CRNN

Chen, T., Wu, X., Li, L., Li, J., & Feng, S. (2022) · Annals of Translational Medicine · doi:10.21037/atm-22-1226 · Cited by 8

Text3D: 3D Convolutional Neural Networks for Text Classification

Wang, J., Li, J., & Zhang, Y. (2023) · Electronics · doi:10.3390/electronics12143087 · Cited by 10

Biomarker identification of hepatocellular carcinoma using a methodical literature mining strategy

Chang, N., Dai, H.-J., Shih, Y.-Y., Wu, C.-Y., Rosa, M. A. C. D., Obena, R. P., Chen, Y., Hsu, W., & Oyang, Y. (2017) · Database · doi:10.1093/database/bax082 · Cited by 9

Crash Prediction Using Deep Learning in a Disorienting Spaceflight Analog Balancing Task

Wang, Y., Tang, J., Vimal, V. P., Lackner, J. R., DiZio, P. A., & Hong, P. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.806357 · Cited by 10

Data Enhancement of Underwater High-Speed Vehicle Echo Signals Based on Improved Generative Adversarial Networks

Wang, Z., Liu, L., Wang, C., Deng, J., Zhang, K., Yang, Y., & Zhou, J. (2022) · Electronics · doi:10.3390/electronics11152310 · Cited by 9

Increasing comprehensiveness and reducing workload in a systematic review of complex interventions using automated machine learning

Uthman, O. A., Court, R. A., Enderby, J., Al-Khudairy, L., Nduka, C. U., Mistry, H., Meléndez‐Torres, G. J., Taylor‐Phillips, S., & Clarke, A. (2022) · Health Technology Assessment · doi:10.3310/udir6682 · Cited by 11

Human-annotated rationales and explainable text classification: a survey

Herrewijnen, E., Nguyen, D., Bex, F., & van Deemter, K. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1260952 · Cited by 11

AdCOFE: Advanced Contextual Feature Extraction in conversations for emotion classification

Bhat, V., Yadav, A., Yadav, S., Chandrasekaran, D., & Mago, V. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.786 · Cited by 8

Rejecting Novel Motions in High-Density Myoelectric Pattern Recognition Using Hybrid Neural Networks

Wu, L., Chen, X., Chen, X. '., & Zhang, X. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.862193 · Cited by 9

The Classification of Music and Art Genres under the Visual Threshold of Deep Learning

Zheng, Z. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4439738 · Cited by 11

Entity relation extraction in the medical domain: based on data augmentation

Wang, A., Li, L., Wu, X., Jianping, Z., Yu, S., Chen, X., Li, J., & Zhu, H. (2022) · Annals of Translational Medicine · doi:10.21037/atm-22-3991 · Cited by 7

Chronic disease diagnosis model based on convolutional neural network and ensemble learning method

Zhou, H., Zhang, P.-Y., Zou, X., Liu, J., & Wang, W. (2023) · Digital Health · doi:10.1177/20552076231198643 · Cited by 9

Language-Based Process Phase Detection in the Trauma Resuscitation

Gu, Y., Li, X., Chen, S., Li, H., Farneth, R. A., Marsic, I., & Burd, R. S. (2017) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2017.50 · Cited by 9

Multi-Dimensional Explanation of Target Variables from Documents

Antognini, D., Musat, C., & Faltings, B. V. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17483 · Cited by 9

Exposure to anti-Black Lives Matter movement and obesity of the Black population

Park, H. J., Francisco, S. C., Pang, M. R., Peng, L., & Chi, G. (2021) · Social Science & Medicine · doi:10.1016/j.socscimed.2021.114265 · Cited by 9

ToxCCIn: Toxic Content Classification with InterpretabilityPreprint

Xiang, T., MacAvaney, S., Yang, E., & Goharian, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.01328 · Cited by 10

Predicting Cryptocurrency Prices During Periods of Conflict: A Comparative Sentiment Analysis Using SVM, CNN-LSTM, and Pysentimento

Rateb, M. N., Alansary, S., Elzouka, M. K., & Galal, M. (2024) · Operations Research Forum · doi:10.1007/s43069-024-00352-6 · Cited by 7

A Fusion NLP Model for the Inference of Standardized Thyroid Nodule Malignancy Scores from Radiology Report Text.

Santos, T., Kallas, O. N., Newsome, J. M., Rubin, D. L., Gichoya, J. W., & Banerjee, I. (2021) · PubMed Central · Cited by 7

Bat4RCT: A suite of benchmark data and baseline methods for text classification of randomized controlled trials

Kim, J., Kim, J., Lee, A., & Kim, J. (2023) · PLoS ONE · doi:10.1371/journal.pone.0283342 · Cited by 9

Food safety news events classification via a hierarchical transformer model

Xiong, S., Tian, W., Batra, V., Fan, X., Xi, L., Liu, H., & Liu, L. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17806 · Cited by 9

An Interpretable Generative Adversarial Approach to Classification of Latent Entity Relations in Unstructured Sentences

Hsu, S. T., Moon, C., Jones, P., & Samatova, N. F. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11972 · Cited by 7

XiaoA: A Robot Editor for Popularity Prediction of Online News Based on Ensemble Learning

Long, F., Xu, M., Li, Y., Wu, Z., & Ling, Q. (2018) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-01313-4_36 · Cited by 7

PALRACE: Reading Comprehension Dataset with Human Data and Labeled RationalesPreprint

Zou, J., Zhang, Y., Jin, P., Luo, C., Pan, X., & Ding, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.12373 · Cited by 6

An Information Retrieval Approach to Building Datasets for Hate Speech DetectionPreprint

Rahman, M. M., Balakrishnan, D., Murthy, D., Kutlu, M., & Lease, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.09775 · Cited by 16

From outputs to insights: a survey of rationalization approaches for explainable text classification

Guzman, E. M., Schlegel, V., & Batista-Navarro, R. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1363531 · Cited by 8

Evaluating Shallow and Deep Learning Strategies for Legal Text Classification of Clauses in Non-Disclosure Agreements

McCarroll, N., McShane, P., O’Connell, E., Curran, K., Singh, M., McNamee, E., Clist, A., & Brammer, A. (2025) · SN Computer Science · doi:10.1007/s42979-025-04300-x · Cited by 6

Leveraging explainability for discussion forum classification: Using confusion detection as an example

Du, H., & Xing, W. (2023) · Distance Education · doi:10.1080/01587919.2022.2150145 · Cited by 9

LERCause: Deep learning approaches for causal sentence identification from nuclear safety reports

Kim, J., Kim, J., Lee, A., Kim, J., & Diesner, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0308155 · Cited by 8

Chinese text classification method based on sentence information enhancement and feature fusion

Zhu, B., & Pan, W. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36861 · Cited by 10

A Self-Attentive Convolutional Neural Networks for Emotion Classification on User-Generated Contents

Qian, Y., Liu, W., & Huang, J. (2019) · IEEE Access · doi:10.1109/access.2019.2938560 · Cited by 8

A Web News Classification Method: Fusion Noise Filtering and Convolutional Neural Network

He, C., Hu, Y., Zhou, A., Tan, Z., Zhang, C., & Ge, B. (2020) · International Symposium on Signal Processing Systems (SSPS) · doi:10.1145/3421515.3421523 · Cited by 6

Stance Detection Based on User Feature Fusion

Huang, W., Wang, Y., Jin-yuan, Y., & Xu, Y. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/5738404 · Cited by 6

An Augmented Neural Network for Sentiment Analysis Using Grammar

Zhang, B., Zhang, H., Shang, J., & Cai, J. (2022) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2022.897402 · Cited by 6

Are Multilingual Sentiment Models Equally Right for the Right Reasons?

Jørgensen, R. L., Caccavale, F., Igel, C., & Søgaard, A. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.11 · Cited by 6

How rationals boost textual entailment modeling: Insights from large language models

Pham, D.-H., Le, T., & Nguyen, H. T. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109517 · Cited by 7

A Two-Step Neural Dialog State Tracker for Task-Oriented Dialog Processing

Kim, A.-Y., Song, H.-J., & Park, S.-B. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/5798684 · Cited by 6

Weakly Supervised Domain Detection

Xu, Y., & Lapata, M. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00287 · Cited by 6

Deep Learning from Incomplete Data: Detecting Imminent Risk of Hospital-acquired Pneumonia in ICU Patients.

Goodwin, T. R., & Demner‐Fushman, D. (2019) · PubMed Central · Cited by 6

Sentence Representation

Liu, Z., Lin, Y., & Sun, M. (2020) · Springer eBooks · doi:10.1007/978-981-15-5573-2_4 · Cited by 7

Enhancing semantical text understanding with fine-tuned large language models: A case study on Quora Question Pair duplicate identification

Han, S., Shi, L., & Tsui, F. R. (2025) · PLoS ONE · doi:10.1371/journal.pone.0317042 · Cited by 6

RoBIn: A Transformer-based model for risk of bias inference with machine reading comprehension

Dias, A. C., Moreira, V. P., & Comba, J. L. D. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104819 · Cited by 6

Rationale-based Human-in-the-Loop via Supervised Attention.

Kanchinadam, T., Westpfahl, K., You, Q., & Fung, G. (2020) · Knowledge Discovery and Data Mining · Cited by 5

Supervised Attention in Sequence-to-Sequence Models for Speech Recognition

Yang, G.-P., & Tang, H. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746310 · Cited by 5

Weakly Supervised Chinese Short Text Classification Algorithm Based on ConWea Model

Wang, S., Duan, C., & Yang, Y. (2022) · International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE) · doi:10.1109/icatiece56365.2022.10047503 · Cited by 5

Temporal dynamics of user activities: deep learning strategies and mathematical modeling for long-term and short-term profiling

Kayed, M., Azzam, F., Ali, H., & Ali, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-64120-6 · Cited by 6

Making the Best Use of Review Summary for Sentiment Analysis

Yang, S., Cui, L., Xie, J., & Zhang, Y. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.15 · Cited by 6

Human-like Explanation for Text Classification With Limited Attention Supervision

Zhang, D., Şen, C., Thadajarassiri, J., Hartvigsen, T., Kong, X., & Rundensteiner, E. A. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671444 · Cited by 5

A data-driven model for early prediction of need for invasive mechanical ventilation in pediatric intensive care unit patients

Bose, S. N., Defante, A., Greenstein, J. L., Haddad, G. G., Ryu, J., & Winslow, R. L. (2023) · PLoS ONE · doi:10.1371/journal.pone.0289763 · Cited by 5

Explainable Text Classification for Legal Document Review in Construction Delay Disputes

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