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

(2014) · Cited by 33,419

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

Detection of Depression and Suicidal Ideation on Social Media: An Intrinsic Review

Madkar, S., Maheshwari, T., Merani, M., Merchant, R., & Doshi, P. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-88244-0_7 · Cited by 4

Perturbation-Based Self-Supervised Attention for Attention Bias in Text Classification

Feng, H., Lin, Z., & Ma, Q. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3302230 · Cited by 7

Context-Aware Feature Selection and Classification

Wang, J., & Bilgic, M. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/480 · Cited by 5

A multi-pronged accurate approach to optical character recognition, using nearest neighborhood and neural-network-based principles

Kumar, G., Kumar, R. R., Ram Chakka, A., & Viswanath, P. (2021) · Sadhana · doi:10.1007/s12046-021-01703-3 · Cited by 4

Practical Benefits of Feature Feedback Under Distribution Shift

Katakkar, A., Yoo, C. H., Wang, W., Lipton, Z. C., & Kaushik, D. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.29 · Cited by 5

User Interface for Managing and Refining Related Patent Terms

Showkatramani, G. J., Krishna, A., Jin, Y., Pepe, A. A., Nula, N., & Gabel, G. (2018) · Communications in computer and information science · doi:10.1007/978-3-319-92270-6_16 · Cited by 4

A Novel Architecture for Automatic Document Classification for Effective Security in Edge Computing Environments

Ding, L., & Salem, M. B. (2018) · IEEE/ACM Symposium on Edge Computing (SEC) · doi:10.1109/sec.2018.00056 · Cited by 4

Enhancing Attention’s Explanation Using Interpretable Tsetlin Machine

Yadav, R. K., & Nicolae, D. (2022) · Algorithms · doi:10.3390/a15050143 · Cited by 4

A computable case definition for patients with SARS-CoV2 testing that occurred outside the hospital

Wang, L., Zipursky, A. R., Geva, A., McMurry, A., Mandl, K. D., & Miller, T. A. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad047 · Cited by 3

A 5G Slice Traffic Anomaly Detection Method Based on Convolution Neural Network

Lu, C., Zhang, T., & Ma, Y. (2023) · doi:10.1109/icsip57908.2023.10270927 · Cited by 3

CNN-based framework for classifying temporal relations with question encoder

Seki, Y., Zhao, K., Oguni, M., & Sugiyama, K. (2021) · International Journal on Digital Libraries · doi:10.1007/s00799-021-00310-1 · Cited by 3

Epidemiologic Information Discovery from Open-Access COVID-19 Case Reports Via Pretrained Language Model

Wang, Z., Liu, X. F., Du, Z., Wang, L., Wu, Y., Holme, P., Lachmann, M., Lin, H., Wong, Z. S.-Y., Xu, X.-K., et al. (2022) · iScience · doi:10.1016/j.isci.2022.105079 · Cited by 5

Ranking-Constrained Learning with Rationales for Text Classification

Wang, J., Sharma, M., & Bilgic, M. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.161 · Cited by 3

What Does the Evidence Say? Models to Help Make Sense of the Biomedical Literature

Wallace, B. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/899 · Cited by 3

REFER: An End-to-end Rationale Extraction Framework for Explanation Regularization

Madani, M. R. G., & Minervini, P. (2023) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-1.40 · Cited by 5

FLMatchQA: a recursive neural network-based question answering with customized federated learning model

Saranya, M., & Amutha, B. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2092 · Cited by 2

ERNIE-TextCNN: research on classification methods of Chinese news headlines in different situations

Yan, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-14955-4 · Cited by 2

Mitigating the Inconsistency Between Word Saliency and Model Confidence with Pathological Contrastive Training

Zhan, P., Wu, Y., Zhou, S., Zhang, Y., & Wang, L. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.175 · Cited by 2

Text Sentiment Analysis Based on Parallel Recursive Constituency Tree-LSTM

Wang, Y., Zhang, X., Wang, X., Zhu, R., Wang, Z., & Liu, L. (2019) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc.2019.00031 · Cited by 2

A Novel Text Representation Model to Categorize Text Documents using Convolution Neural Network

Revanasiddappa, M. B., & Harish, B. S. (2019) · International Journal of Intelligent Systems and Applications · doi:10.5815/ijisa.2019.05.05 · Cited by 2

Linguistic Pattern–Infused Dual-Channel Bidirectional Long Short-term Memory With Attention for Dengue Case Summary Generation From the Program for Monitoring Emerging Diseases–Mail Database: Algorithm Development Study

Chang, Y., Chiu, Y.-W., & Chuang, T. (2022) · JMIR Public Health and Surveillance · doi:10.2196/34583 · Cited by 5

Deep Neural Networks Evolve Human-like Attention Distribution during Goal-directed Reading ComprehensionPreprint

Zou, J., & Ding, N. (2021) · Research Square · doi:10.21203/rs.3.rs-813994/v1 · Cited by 2

Simulation of climate change and thinning effects on Central European beech forests using 3-PGmix

Suliman, T., Ciceu, A., Kašpar, J., Vašíčková, I., Missarov, A., & Santini, M. (2026) · European Journal of Forest Research · doi:10.1007/s10342-025-01851-9 · Cited by 1

Ld-CNNs: A Deep Learning System for Structured Text Categorization Based on LDA in Content Security

Liu, J., Xu, Y., Deng, J., Wang, L., & Zhang, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46298-1_8 · Cited by 1

Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases

Chalkidis, I., Fergadiotis, M., Tsarapatsanis, D., Αλέτρας, Ν., Androutsopoulos, I., & Malakasiotis, P. (2021) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · doi:10.48550/arxiv.2103.13084 · Cited by 21

Few-Shot Text Classification with External Knowledge Expansion

Guan, J., Xu, R. X., Ya, J., Tang, Q., Xue, J., & Zhang, N. (2021) · International Conference on Innovation in Artificial Intelligence (ICIAI) · doi:10.1145/3461353.3461389 · Cited by 1

Comparing Deep Learning and Conventional Machine Learning Models for Predicting Mental Illness from History of Present Illness Notations.

Shrestha, I., & Srinivasan, P. (2021) · PubMed Central · Cited by 1

Review: A Roadmap to Use Nonstructured Data to Discover Multitarget Cancer Therapies

Scoarta, S., Küçükosmanoğlu, A., Bindt, F., Pouwer, M. G., & Westerman, B. A. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00096 · Cited by 2

Active neural learners for text with dual supervision

Sastry, C. S., & Milios, E. (2020) · Neural Computing and Applications · doi:10.1007/s00521-019-04681-0 · Cited by 1

Identifying current Juul users among emerging adults through Twitter feeds

Tran, T., Ickes, M. J., Hester, J. W., & Kavuluru, R. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104350 · Cited by 1

Making Document-Level Information Extraction Right for the Right ReasonsPreprint

Tang, L., Rajan, D., Mohan, S., Pradhan, A., Bryan, R. N., & Durrett, G. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07686 · Cited by 2

Modeling text embedded information cascades

Xu, S. (2019) · doi:10.17760/d20328884 · Cited by 1

Cryptocurrency Price Forecasting Implementing Sentiment Analysis during the Russian-Ukrainian WarPreprint

Rateb, M. N., Alansary, S., Elzouka, M. K., & Galal, M. (2024) · Research Square · doi:10.21203/rs.3.rs-3835106/v1 · Cited by 2

Multilevel Heuristics for Rationale-Based Entity Relation Classification in Sentences

Hsu, S. T., Chaudhary, M. S., & Samatova, N. F. (2018) · International Conference on Computational Linguistics

Training Machine Learning Models by Regularizing their ExplanationsPreprint

Ross, A. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.00869

Automatic Approval of Online Comments with Multiple-Encoder Networks

Dang, V. M. H. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-6168-9_20

SIM-ECG: A Signal Importance Mask-driven ECGClassification SystemPreprint

Kc, D., Zhang, C., Gniady, C., Agarwal, P. S., & Sharma, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.14835

Image Classification with Consistent Supporting EvidencePreprint

Wang, P., Liao, R., Moyer, D., Berkowitz, S. J., Horng, S., & Golland, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.07048

Contrapositive Local Class Inference

Kashefi, O., & Hwa, R. (2021) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/2021.wnut-1.41

Functional annotation of creeping bentgrass protein sequences based on convolutional neural network

Jiang, H., & He, J. Y. D. (2022) · BMC Plant Biology · doi:10.1186/s12870-022-03607-8

Proceedings of the First Workshop on Learning with Natural Language Supervision

(2022) · doi:10.18653/v1/2022.lnls-1

The model thinks what?! Interpreting deep NLP models with rationales and influence

Jain, S. (2022) · Digital Repository Service (Northeastern University) · doi:10.17760/d20467212

A review on devices and learning techniques in domestic intelligent environment

Ye, J., Wang, M., Zhong, J., & Jiang, H. (2024) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-024-04759-1

Cryptocurrency Price Prediction: A Comparative Sentiment Analysis Approach Using SVM, CNN-LSTM, and Pysentimento during Times of CrisisPreprint

Rateb, M. N., Alansary, S., Elzouka, M. K., & Galal, M. (2024) · Research Square · doi:10.21203/rs.3.rs-3982656/v1

БУДУЋНОСТ ОБРАЗОВАЊА: ВАЖНОСТ ЕТИЧКИХ ОКВИРА И КУЛТУРНЕ ОСЕТЉИВОСТИ

Baltezarević, B. (2024) · Bastina · doi:10.5937/bastina34-51313

Automating Risk of Bias Inference in Clinical Studies

Dias, A. C., Moreira, V. P., & Comba, J. L. D. (2025) · doi:10.5753/sbcas_estendido.2025.7404

Neural Transfer Learning for Domain Adaptation in Natural Language Processing

Meftah, S. (2021) · theses.fr (ABES) · doi:10.70675/9111562bzfa37z4fd8zae6bz2e8ae8d5d403

IBIS-Net: An iterative bio-inspired selection network for interpretable text classification

Wang, Y., & Todo, Y. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.116047 · Cited by 1

GRACE: Gradient-Regulated Approach for Consistent Explanations

Basu, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-36039-7_33

RANCC: Rationalizing Neural Networks via Concept Clustering

Bashier, H. K., Kim, M., & Goebel, R. G. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.286 · Cited by 5

Machine-annotated Rationales: Faithfully Explaining Text Classification

Herrewijnen, E., Nguyen, D., Mense, J. P., Bex, F., Systems, S. I., Processing, S. N. L., & Systems, I. (2021) · Utrecht University Repository (Utrecht University) · Cited by 1

SLCNN: Sentence-Level Convolutional Neural Network for Text ClassificationPreprint

Jarrahi, A., Mousa, R., & Safari, L. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2301.11696 · Cited by 2

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

An analysis of convolutional neural networks for sentence classification

Vieira, J. P. A., & Moura, R. S. (2017) · Latin American Computer Conference (CLEI) · doi:10.1109/clei.2017.8226381 · Cited by 59