Cited by 14,831Topic ModelingSentiment Analysis and Opinion MiningAdvanced Neural Network Applications

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Convolutional Neural Networks for Sentence Classification

Open access

Kim, Yoon·2014·Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Open this paper in CoChat — free account Open PDF

Abstract

We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks.Learning task-specific vectors through fine-tuning offers further gains in performance.We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors.The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.3115/v1/d14-1181

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
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
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Untitled work
(2010) · Cited by 8,261
Mining and summarizing customer reviews
Hu, M., & Liu, B. (2004) · Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1014052.1014073 · Cited by 7,793
Natural Language Processing (almost) from ScratchPreprint
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 6,923
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322

Show all 33 references

Research works printed but not linked: 4

Y. Bengio, R. Ducharme, P. Vincent. 2003. Neu- ral Probabilitistic Language Model. Journal of Ma- chine Learning Research 3:1137-1155.
J. Duchi, E. Hazan, Y. Singer. 2011 Adaptive subgra- dient methods for online learning and stochastic op- timization. Journal of Machine Learning Research, 12:2121-2159.
A. Krizhevsky, I. Sutskever, G. Hinton. 2012. Ima- geNet Classification with Deep Convolutional Neu- ral Networks. In Proceedings of NIPS 2012.
Q. Le, T. Mikolov. 2014. Distributed Represenations of Sentences and Documents. In Proceedings of ICML 2014.

Sorted by citations

Items 101–166 of 166

SECTOR: A Neural Model for Coherent Topic Segmentation and Classification
Arnold, S., Schneider, R., Cudré-Mauroux, P., Gers, F. A., & Löser, A. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00261 · Cited by 85
LearningQ: A Large-Scale Dataset for Educational Question Generation
Chen, G., Yang, J., Hauff, C., & Houben, G.-J. (2018) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v12i1.14987 · Cited by 83
Abusive language detection from social media comments using conventional machine learning and deep learning approaches
Akhter, M. P., Jiangbin, Z., Naqvi, I. R., AbdelMajeed, M., & Zia, T. (2022) · Multimedia Systems · doi:10.1007/s00530-021-00784-8 · Cited by 84
BERT-based ensemble learning for multi-aspect hate speech detection
Mazari, A. C., Boudoukhani, N., & Djeffal, A. (2024) · Cluster Computing · doi:10.1007/s10586-022-03956-x · Cited by 89
A Robust Sentiment Analysis Method Based on Sequential Combination of Convolutional and Recursive Neural Networks
Sadr, H., Pedram, M. M., & Teshnehlab, M. (2019) · Neural Processing Letters · doi:10.1007/s11063-019-10049-1 · Cited by 82
Cyberbullying detection in social media text based on character-level convolutional neural network with shortcuts
Lu, N., Wu, G., Zhang, Z., Zheng, Y., Ren, Y., & Choo, K. R. (2020) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.5627 · Cited by 76
Mapping global dynamics of benchmark creation and saturation in artificial intelligence
Ott, S., Barbosa-Silva, A., Blagec, K., Brauner, J., & Samwald, M. (2022) · Nature Communications · doi:10.1038/s41467-022-34591-0 · Cited by 73
Using virtual standardized patients to accurately assess information gathering skills in medical students
Maicher, K. R., Zimmerman, L., Wilcox, B., Liston, B., Cronau, H., Macerollo, A., Jin, L., Jaffe, E., White, M., Fosler-Lussier, E., et al. (2019) · Medical Teacher · doi:10.1080/0142159x.2019.1616683 · Cited by 72
Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism
Fang, Y., Yang, S., Zhao, B., & Huang, C. (2021) · Information · doi:10.3390/info12040171 · Cited by 74
“TF-IDF + SVM 0.952 0.945 0.947 Char N-grams + LR [21] 0.947 0.943 0.944 Multi-features + LR [24] 0.955 0.948 0.949 KimCNN [59] 0.946 0.946 0.946 Bi-LSTM + Attention [29] 0.935 0.937 0.936 Proposed Method 0.961 0.960 0.960 Wikipedia [58] TF-IDF + SVM 0.923 0.903 0.910 Char N-grams + LR [21] 0.928 0.904 0.912 Multi-feat…”
MethodInfluential
AD-BERT: Using pre-trained language model to predict the progression from mild cognitive impairment to Alzheimer's disease
Mao, C., Xu, J., Rasmussen, L., Li, Y., Adekkanattu, P., Pacheco, J., Bonakdarpour, B., Vassar, R., Shen, L., Jiang, G., et al. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104442 · Cited by 67
Deep Convolutional Neural Network‐Based Structural Damage Localization and Quantification Using Transmissibility Data
Cofre-Martel, S., Kobrich, P., Lopez Droguett, E., & Meruane, V. (2019) · Shock and Vibration · doi:10.1155/2019/9859281 · Cited by 65
Rapid identification of pathogens by using surface-enhanced Raman spectroscopy and multi-scale convolutional neural network
Ding, J., Lin, Q., Zhang, J., Young, G. M., Jiang, C., Zhong, Y., & Zhang, J. (2021) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-021-03332-5 · Cited by 61
Improving clinical outcome predictions using convolution over medical entities with multimodal learning
Bardak, B., & Tan, M. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102112 · Cited by 48
Grey-box Adversarial Attack And Defence For Sentiment Classification
Xu, Y., Zhong, X., Jimeno Yepes, A., & Lau, J. H. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.321 · Cited by 54
State of charge estimation of an electric vehicle's battery using tiny neural network embedded on small microcontroller units
Mazzi, Y., Ben Sassi, H., Gaga, A., & Errahimi, F. (2022) · International Journal of Energy Research · doi:10.1002/er.7713 · Cited by 53
Sliding into My DMs: Detecting Uncomfortable or Unsafe Sexual Risk Experiences within Instagram Direct Messages Grounded in the Perspective of Youth
Razi, A., Alsoubai, A., Kim, S., Ali, S., Stringhini, G., De Choudhury, M., & Wisniewski, P. J. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3579522 · Cited by 52
Explainable Sentiment Analysis with Applications in Medicine
Zucco, C., Liang, H., Fatta, G. D., & Cannataro, M. (2018) · 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621359 · Cited by 59
Automated Emerging Cyber Threat Identification and Profiling Based on Natural Language Processing
Marinho, R., & Holanda, R. (2023) · IEEE Access · doi:10.1109/access.2023.3260020 · Cited by 50
Contrastive Learning Models for Sentence Representations
Xu, L., Xie, H., Li, Z., Wang, F. L., Wang, W., & Li, Q. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3593590 · Cited by 50
Early prediction of diagnostic-related groups and estimation of hospital cost by processing clinical notes
Liu, J., Capurro, D., Nguyen, A., & Verspoor, K. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00474-9 · Cited by 50
Can Neural Machine Translation be Improved with User Feedback?
Kreutzer, J., Khadivi, S., Matusov, E., & Riezler, S. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers) · doi:10.18653/v1/n18-3012 · Cited by 49
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
Kong, L., Jiang, H., Zhuang, Y., Lyu, J., Zhao, T., & Zhang, C. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.102 · Cited by 58
AI-based ICD coding and classification approaches using discharge summaries: A systematic literature review
Kaur, R., Ginige, J. A., & Obst, O. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118997 · Cited by 48
Scaling up data curation using deep learning: An application to literature triage in genomic variation resources
Lee, K., Famiglietti, M. L., McMahon, A., Wei, C.-H., MacArthur, J. A. L., Poux, S., Breuza, L., Bridge, A., Cunningham, F., Xenarios, I., et al. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006390 · Cited by 47
Generating Question-Answer Hierarchies
Krishna, K., & Iyyer, M. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1224 · Cited by 44
Learning towards conversational AI: A survey
Fu, T., Gao, S., Zhao, X., Wen, J.-R., & Yan, R. (2022) · AI Open · doi:10.1016/j.aiopen.2022.02.001 · Cited by 46
A Novel System for Extractive Clinical Note Summarization using
Liang, J., Tsou, C.-H., & Poddar, A. (2019) · Proceedings of the 2nd Clinical Natural Language Processing Workshop · doi:10.18653/v1/w19-1906 · Cited by 44
Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning
Kreutzer, J., Uyheng, J., & Riezler, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1165 · Cited by 38
Modeling temporal patterns of cyberbullying detection with hierarchical attention networks
Cheng, L., Guo, R., Silva, Y. N., Hall, D., & Liu, H. (2021) · ACM/IMS Transactions on Data Science · doi:10.1145/3441141 · Cited by 43
Explainable artificial intelligence, lawyer's perspective
Górski, Ł., & Ramakrishna, S. (2021) · Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law · doi:10.1145/3462757.3466145 · Cited by 40
Foundations of Multimodal Co-learning
Zadeh, A., Liang, P. P., & Morency, L.-P. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.06.001 · Cited by 40
Natural Language Processing in Marketing
Hartmann, J., & Netzer, O. (2023) · Artificial Intelligence in Marketing · doi:10.1108/s1548-643520230000020011 · Cited by 38
COVID-19 Tweets Classification Based on a Hybrid Word Embedding Method
Didi, Y., Walha, A., & Wali, A. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6020058 · Cited by 38
Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation
Lucas, J., Uchendu, A., Yamashita, M., Lee, J., Rohatgi, S., & Lee, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.883 · Cited by 37
From 'Friends with Benefits' to 'Sextortion:' A Nuanced Investigation of Adolescents' Online Sexual Risk Experiences
Alsoubai, A., Song, J., Razi, A., Naher, N., De Choudhury, M., & Wisniewski, P. J. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555136 · Cited by 36
Deep learning for online harassment detection in tweets
Marwa, T., Salima, O., & Souham, M. (2018) · 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS) · doi:10.1109/pais.2018.8598530 · Cited by 35
Convolutional Neural Networks for Unsupervised Anomaly Detection in Text Data
Gorokhov, O., Petrovskiy, M., & Mashechkin, I. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-68935-7_54 · Cited by 34
An Investigation of Transfer Learning-Based Sentiment Analysis in Japanese
Bataa, E., & Wu, J. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1458 · Cited by 35
Intrinsic bias metrics do not correlate with application bias
Goldfarb-Tarrant, S., Marchant, R., Muñoz Sánchez, R., Pandya, M., & Lopez, A. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.150 · Cited by 33
Detection of adversarial examples in text classification: Benchmark and baseline via robust density estimation
Yoo, K., Kim, J., Jang, J., & Kwak, N. (2022) · Findings of the Association for Computational Linguistics: ACL 2022 · doi:10.18653/v1/2022.findings-acl.289 · Cited by 33
Building towards Automated Cyberbullying Detection: A Comparative Analysis
Al-Harigy, L. M., Al-Nuaim, H. A., Moradpoor, N., & Tan, Z. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4794227 · Cited by 32
Towards employing native information in citation function classification
Zhang, Y., Zhao, R., Wang, Y., Chen, H., Mahmood, A., Zaib, M., Zhang, W. E., & Sheng, Q. Z. (2022) · Scientometrics · doi:10.1007/s11192-021-04242-0 · Cited by 28
Optimizing the recency-relevance-diversity trade-offs in non-personalized news recommendations
Chakraborty, A., Ghosh, S., Ganguly, N., & Gummadi, K. P. (2019) · Information Retrieval Journal · doi:10.1007/s10791-019-09351-2 · Cited by 24
Aceso: PICO-Guided Evidence Summarization on Medical Literature.
Zhang, X., Geng, P., Zhang, T., Lu, Q., Gao, P., & Mei, J. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2984704 · Cited by 25
Transformer network-based word embeddings approach for autonomous cyberbullying detection
Pericherla, S., & Ilavarasan, E. (2024) · International Journal of Intelligent Unmanned Systems · doi:10.1108/ijius-02-2021-0011 · Cited by 23
Convolutional neural networks for multi-topic dialog state tracking,
Shi, H., Ushio, T., Endo, M., Yamagami, K., & Horii, N. (2017) · Lecture Notes in Electrical Engineering · doi:10.1007/978-981-10-2585-3_37 · Cited by 24
Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques
Coyac-Torres, J. E., Sidorov, G., Aguirre-Anaya, E., & Hernández-Oregón, G. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5030058 · Cited by 23
Quantum deep learning neural networks
Kamruzzaman, A., Alhwaiti, Y., Leider, A., & Tappert, C. C. (2020) · Lecture notes in networks and systems · doi:10.1007/978-3-030-12385-7_24 · Cited by 26
Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records
Chen, Q., Du, J., Kim, S., Wilbur, W. J., & Lu, Z. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1044-0 · Cited by 19
Improving Joint Learning of Chest X-Ray and Radiology Report by Word Region Alignment
Ji, Z., Shaikh, M. A., Moukheiber, D., Srihari, S. N., Peng, Y., & Gao, M. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87589-3_12 · Cited by 21
Reducing annotation burden through multimodal learning
Lopez, K., Fodeh, S. J., Allam, A., Brandt, C. A., & Krauthammer, M. (2020) · Frontiers in Big Data · doi:10.3389/fdata.2020.00019 · Cited by 21
A happiness degree predictor using the conceptual data structure for deep learning architectures
Pérez-Benito, F. J., Villacampa-Fernández, P., Conejero, J. A., García-Gómez, J. M., & Navarro-Pardo, E. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.11.004 · Cited by 20
LGCCT: A Light Gated and Crossed Complementation Transformer for Multimodal Speech Emotion Recognition
Liu, F., Shen, S.-Y., Fu, Z.-W., Wang, H.-Y., Zhou, A.-M., & Qi, J.-Y. (2022) · Entropy · doi:10.3390/e24071010 · Cited by 21
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.06904 · Cited by 20
Learning Invariant Representations of Social Media Users
Andrews, N., & Bishop, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1178 · Cited by 19
An Intelligent Virtual Standard Patient for Medical Students Training Based on Oral Knowledge Graph
Song, W., Hou, X., Li, S., Chen, C., Gao, D., Wang, X., Sun, Y., Hou, J., & Hao, A. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3205456 · Cited by 18
Question Generation With Doubly Adversarial Nets
Bao, J., Gong, Y., Duan, N., Zhou, M., & Zhao, T. (2018) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2018.2859777 · Cited by 14
LS-Tree: Model Interpretation When the Data Are Linguistic
Chen, J., & Jordan, M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5749 · Cited by 14
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
Comparing Literature- and Subreddit-Derived Laboratory Values in Polycystic Ovary Syndrome (PCOS): Validation of Clinical Data Posted on PCOS Reddit Forums
Emanuel, R. H. K., Docherty, P. D., Lunt, H., & Campbell, R. E. (2023) · JMIR Formative Research · doi:10.2196/44810 · Cited by 9
Alquist 3.0: Alexa Prize Bot Using Conversational Knowledge Graph
Pichl, J., Marek, P., Konrád, J., Lorenc, P., Ta, V. D., & Šedivý, J. (2020) · arXiv · doi:10.48550/arxiv.2011.03261 · Cited by 7
Using deep learning to identify translational research in genomic medicine beyond bench to bedside
Hsu, Y.-Y., Clyne, M., Wei, C.-H., Khoury, M. J., & Lu, Z. (2019) · Database · doi:10.1093/database/baz010 · Cited by 6
BDCN: Semantic Embedding Self-explanatory Breast Diagnostic Capsules Network
Chen, D., Zhong, K., & He, J. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-84186-7_28 · Cited by 3
AI-powered knowledge organization: A next-generation approach to library classification using DeepSeek-R1
Feng, N. (2025) · Scientific Reports · doi:10.1038/s41598-025-22272-z · Cited by 3
Cost-sensitive PLM-based Approach for Arabic and English Cyberbullying Classification
Harbaoui, A., Benaissa, A. R., & ghezala, H. B. (2023) · doi:10.21203/rs.3.rs-2524732/v1 · Cited by 3
Quantifying and Localizing Usable Information Leakage from Neural Network Gradients
Mo, F., Borovykh, A., Malekzadeh, M., Demetriou, S., Gündüz, D., & Haddadi, H. (2021) · arXiv · doi:10.48550/arxiv.2105.13929 · Cited by 1
“TextClf [62] C16(3)-P(2)-C16(8)-P(2)-F50-O IMDB & CSI”

Authors: Kim, Y.
Affiliations: New York University (US)
Corresponding author: Kim, Yoon
ORCID: Kim, Yoon
Venue: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Publisher: Association for Computational Linguistics
Published: 2014
Pages: 1746-1751
DOI: 10.3115/v1/d14-1181
OpenAlex: W1832693441
Cited by: 14,831
References: 33
Citations by year: 2026: 565 · 2025: 910 · 2024: 1113 · 2023: 1406 · 2022: 1592 · 2021: 2115 · 2020: 2260 · 2019: 2134 · 2018: 1532 · 2017: 755 · 2016: 334 · 2015: 115
FWCI: 236.23
Type: conference paper
Language: en
Open access: hybrid
doi.org
Licence: cc-by
Locations: Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: convolutional neural networks, sentence classification, pre-trained word vectors, static embeddings, fine-tuning, sentiment analysis, question classification

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

Convolutional Neural Networks for Sentence Classification

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks