The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Enriching Word Vectors with Subword Information
Open accessAbstract
Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram; words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows us to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks.
Overview
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
Cites
Statistics
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Contrary to string similarity-based approaches, continuous word representations capture not only lexical but also semantic properties of tokens.”
From A Method for the Calculation of Parallel Passages for Buddhist Chinese Sources Based on Million-scale Nearest Neighbor Search (2020) · cites this paper as Bojanowski et al. (2017)
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We screened 4 different word embeddings following a similar procedure reported in our previous study [46] and found that the Common Crawl embeddings—released by Facebook and trained using the fastText on the Common Crawl data set [47]—achieved better performance compared to other embeddings on a validation data set.”
From Extracting Family History of Patients From Clinical Narratives: Exploring an End-to-End Solution With Deep Learning Models (2020) · cites this paper as [47]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“I use Fasttext (Bojanowski et al. 2017) as a word representation model because of its capacity to collect sub-word information, which can be helpful in the case of compound words.”
From A Method for the Calculation of Parallel Passages for Buddhist Chinese Sources Based on Million-scale Nearest Neighbor Search (2020) · cites this paper as Bojanowski et al. 2017
The cited source says what the claim attributes to it.
In the citing paper
“This technique involves learning large-scale word embeddings (Bojanowski et al. 2017; Joulin et al. 2016), representing an advancement from Word2Vec.”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Bojanowski et al. 2017
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568
Self-citationDistributed 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
Self-citationThe proof and measurement of association between two things
Spearman, C. (1904) · The American Journal of Psychology · doi:10.2307/1412159 · Cited by 8,039
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
Harris, Z. S. (1954) · WORD · doi:10.1080/00437956.1954.11659520 · Cited by 4,807
Character-level Convolutional Networks for Text ClassificationPreprint
Zhang, X., Zhao, J., & LeCun, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.01626 · Cited by 4,481
From Frequency to Meaning: Vector Space Models of Semantics
Turney, P. D., & Pantel, P. (2010) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2934 · Cited by 2,949
Generating Sequences With Recurrent Neural Networks
Graves, A. (2013) · arXiv · doi:10.48550/arxiv.1308.0850 · Cited by 2,894
Producing high-dimensional semantic spaces from lexical co-occurrence
Lund, K., & Burgess, C. (1996) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03204766 · Cited by 1,875
Character-Aware Neural Language Models
Kim, Y., Jernite, Y., Sontag, D. A., & Rush, A. M. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10362 · Cited by 1,590
Research works printed but not linked: 1
- Cui Qing, 2015, ACM Transactions on Information Systems, 34, 1
Cited by
Sorted by citations
Items 501–600 of 1,029
Mohammad, S. M. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821124-3.00011-9 · Cited by 101
Distributed representations of tuples for entity resolution
Ebraheem, M., Thirumuruganathan, S., Joty, S., Ouzzani, M., & Tang, N. (2018) · Proceedings of the VLDB Endowment · doi:10.14778/3236187.3269461 · Cited by 93
Ghosal, S., & Jain, A. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.141 · Cited by 90
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
Lastra-Díaz, J. J., Goikoetxea, J., Taieb, M. A. H., García-Serrano, A. M., Aouicha, M. B., & Agirre, E. (2019) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2019.07.010 · Cited by 94
Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification
Yang, F., Peng, X., Ghosh, G., Shilon, R., Ma, H., Moore, E., & Predović, G. (2019) · Workshop on Abusive Language Online (ALW) · doi:10.18653/v1/w19-3502 · Cited by 94
Deep Graph Matching and Searching for Semantic Code Retrieval
Ling, X., Wu, L., Wang, S., Pan, G., Ma, T., Xu, F., Liu, A. X., Wu, C., & Ji, S. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3447571 · Cited by 97
Knowledge Enhanced Masked Language Model for Stance Detection
Kawintiranon, K., & Singh, L. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.376 · Cited by 93
Baek, S., Jung, W., & Han, S. H. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103915 · Cited by 93
A Comprehensive Review of Arabic Text Summarization
Elsaid, A., Mohammed, A., Ibrahim, L. F., & Sakre, M. (2022) · IEEE Access · doi:10.1109/access.2022.3163292 · Cited by 96
Social media user behavior analysis applied to the fashion and apparel industry in the big data era
Xue, Z., Li, Q., & Zeng, X. (2023) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2023.103299 · Cited by 89
Fake news detection: Taxonomy and comparative study
Farhangian, F., Cruz, R. M. O., & Cavalcanti, G. D. C. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.102140 · Cited by 91
A Survey of Knowledge Enhanced Pre-trained Language Models
Yang, J. K., Hu, X., Xiao, G., & Shen, Y. (2024) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3631392 · Cited by 107
Spherical Text EmbeddingPreprint
Yu, M., Huang, J., Wang, G., Zhang, C., Zhuang, H., Kaplan, L., & Han, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.01196 · Cited by 91
Unsupervised Cross-Lingual Information Retrieval Using Monolingual Data Only
Litschko, R., Glavaš, G., Ponzetto, S. P., & Vulić, I. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3210157 · Cited by 88
Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2vec and WordNet
Orkphol, K., & Wu, Y. J. (2019) · Future Internet · doi:10.3390/fi11050114 · Cited by 91
ATM: Adversarial-neural Topic Model
Wang, R., Zhou, D., & He, Y. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2019.102098 · Cited by 91
Liu, Z., Winata, G. I., Lin, Z., Xu, P., & Fung, P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6362 · Cited by 92
Dynamic Embedding Projection-Gated Convolutional Neural Networks for Text Classification
Tan, Z., Chen, J., Kang, Q., Zhou, M., Abusorrah, A. M., & Sedraoui, K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3036192 · Cited by 92
da Cunha, W. L. M., Mangaravite, V., Gomes, C., Canuto, S., Resende, E., Nascimento, C., Viegas, F. A. R., França, C., Martins, W. S., Almeida, J. M., et al. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2020.102481 · Cited by 92
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models
Shi, Y., Siddharth, N., Paige, B., & Torr, P. H. S. (2019) · arXiv · doi:10.48550/arxiv.1911.03393 · Cited by 186
“For images, similar to training, we use the off-the-shelf ResNet-101 to generate feature vector of dimen-sion 2048-d ; for captions, we fit a FastText model on all sentences in the training set, projecting each word onto a 300-d vector (Bojanowski et al., 2017).”
MethodOn the evaluation and combination of state-of-the-art features in Twitter sentiment analysis
Carvalho, J., & Plastino, A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09895-6 · Cited by 87
Skills2Job: A recommender system that encodes job offer embeddings on graph databases
Giabelli, A., Malandri, L., Mercorio, F., Mezzanzanica, M., & Seveso, A. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.107049 · Cited by 87
SemEval-2021 Task 5: Toxic Spans Detection
Pavlopoulos, J., Sorensen, J. S., Laugier, L., & Androutsopoulos, I. (2021) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2021.semeval-1.6 · Cited by 93
Towards a Cleaner Document-Oriented Multilingual Crawled Corpus
Abadji, J., Suárez, P. O., Romary, L., & Sagot, B. (2022) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/579or68a4ybs · Cited by 150
Parola, A., Lin, J. M., Simonsen, A., Bliksted, V., Zhou, Y., Wang, H., Inoue, L., Koelkebeck, K., & Fusaroli, R. (2022) · Schizophrenia Research · doi:10.1016/j.schres.2022.07.002 · Cited by 87
Unsupervised Cross-Modal Alignment of Speech and Text Embedding SpacesPreprint
Chung, Y.-A., Weng, W., Tong, S., & Glass, J. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.07467 · Cited by 87
Detecting Depression in Social Media using Fine-Grained Emotions
Aragón, M. E., López-Monroy, A. P., González, L. C., & Montes-y-Gómez, M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1151 · Cited by 86
Multi-Task Stance Detection with Sentiment and Stance Lexicons
Li, Y., & Caragea, C. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1657 · Cited by 88
Deep Sequence-to-Sequence Entity Matching for Heterogeneous Entity Resolution
Nie, H., Han, X., He, B., Sun, L., Chen, B., Zhang, W., Wu, S., & Kong, H. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3358018 · Cited by 86
Dong, X. L., He, X., Kan, A., Li, X., Liang, Y., Ma, J., Xu, Y., Zhang, C., Zhao, T., Gabriel, B. S., et al. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403323 · Cited by 90
Bozkır, A. S., Dalgic, F. C., & Aydos, M. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102964 · Cited by 92
Aspect-Based Financial Sentiment Analysis using Deep Learning
Jangid, H., Singhal, S., Shah, R. R., & Zimmermann, R. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3191827 · Cited by 85
Neural network-based approaches for biomedical relation classification: A review
Zhang, Y., Lin, H., Yang, Z., Wang, J., Sun, Y., Xu, B., & Zhao, Z. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103294 · Cited by 88
Intent Classification for Dialogue Utterances
Schuurmans, J. T., & Frăsincar, F. (2019) · IEEE Intelligent Systems · doi:10.1109/mis.2019.2954966 · Cited by 90
PERBANDINGAN KINERJA WORD EMBEDDING WORD2VEC, GLOVE, DAN FASTTEXT PADA KLASIFIKASI TEKS
Nurdin, A., Aji, B. A. S., Bustamin, A., & Abidin, Z. (2020) · Jurnal Tekno Kompak · doi:10.33365/jtk.v14i2.732 · Cited by 85
Efficient estimation of Hindi WSD with distributed word representation in vector space
Kumari, A., & Lobiyal, D. K. (2021) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.03.008 · Cited by 85
Le, N. Q. K. (2019) · Molecular Genetics and Genomics · doi:10.1007/s00438-019-01570-y · Cited by 84
A Survey of Computational Methods for Online Mental State Assessment on Social Media
Ríssola, E. A., Losada, D. E., & Crestani, F. (2021) · ACM Transactions on Computing for Healthcare · doi:10.1145/3437259 · Cited by 88
Representation Learning for Natural Language Processing
Liu, Z., Lin, Y., & Sun, M. (2020) · Springer eBooks · doi:10.1007/978-981-15-5573-2 · Cited by 103
Gender Bias in Word Embeddings
Caliskan, A., Ajay, P. P., Charlesworth, T. E. S., Wolfe, R., & Banaji, M. R. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534162 · Cited by 84
AlexNet architecture based convolutional neural network for toxic comments classification
Singh, I., Goyal, G., & Chandel, A. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.06.007 · Cited by 90
Assessing the quality of the steps to reproduce in bug reports
Chaparro, O., Bernal-Cárdenas, C., Lu, J., Moran, K., Marcus, A., Penta, M. D., Poshyvanyk, D., & Ng, V. (2019) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3338906.3338947 · Cited by 100
Indurthi, V., Syed, B., Shrivastava, M., Chakravartula, N., Gupta, M., & Varma, V. (2019) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/s19-2009 · Cited by 83
Unsupervised Cross-Lingual Representation Learning
Ruder, S., Søgaard, A., & Vulić, I. (2019) · doi:10.18653/v1/p19-4007 · Cited by 83
Rare Words: A Major Problem for Contextualized Embeddings and How to Fix it by Attentive Mimicking
Schick, T., & Schütze, H. (2020) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v34i05.6403 · Cited by 88
Detecting Misogyny and Xenophobia in Spanish Tweets Using Language Technologies
Plaza-del-Arco, F. M., Molina-González, M. D., López, L. A. U., & Valdivia, M. T. M. (2020) · ACM Transactions on Internet Technology · doi:10.1145/3369869 · Cited by 85
Enhancing Arabic aspect-based sentiment analysis using deep learning models
Al-Dabet, S., Tedmori, S., & AL-Smadi, M. (2021) · Computer Speech & Language · doi:10.1016/j.csl.2021.101224 · Cited by 88
Representation learning applications in biological sequence analysis
Iuchi, H., Matsutani, T., Yamada, K., Iwano, N., Sumi, S., Hosoda, S., Zhao, S., Fukunaga, T., & Hamada, M. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.05.039 · Cited by 88
A comparative evaluation and analysis of three generations of Distributional Semantic Models
Lenci, A., Sahlgren, M., Jeuniaux, P. P. J. M. H., Gyllensten, A. C., & Miliani, M. (2022) · Language Resources and Evaluation · doi:10.1007/s10579-021-09575-z · Cited by 97
A Taxonomy of Fake News Classification Techniques: Survey and Implementation Aspects
Rohera, D., Shethna, H., Patel, K., Thakker, U., Tanwar, S., Gupta, R., Hong, W., & Sharma, R. (2022) · IEEE Access · doi:10.1109/access.2022.3159651 · Cited by 92
Surface and Deep Features Ensemble for Sentiment Analysis of Arabic Tweets
Al-Twairesh, N., & Al-Negheimish, H. (2019) · IEEE Access · doi:10.1109/access.2019.2924314 · Cited by 83
A Comparison of Word-Embeddings in Emotion Detection from Text using BiLSTM, CNN and Self-Attention
Polignano, M., Basile, P., de Gemmis, M., & Semeraro, G. M. (2019) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3314183.3324983 · Cited by 88
TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
Shen, J., Shen, Z., Xiong, C., Wang, C., Wang, K., & Han, J. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366423.3380132 · Cited by 89
A deep learning approach in predicting products’ sentiment ratings: a comparative analysis
Balakrishnan, V., Shi, Z., Law, C. L., Lim, R., Teh, L. L., & Fan, Y. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-04169-6 · Cited by 84
Email spam detection using hierarchical attention hybrid deep learning method
Zavrak, S., & Yılmaz, Ş. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120977 · Cited by 89
Bereczki, Z., Benczik, B., Balogh, O. M., Marton, S., Puhl, E., Pétervári, M., Váczy‐Földi, M., Papp, Z. T., Makkos, A., Glass, K., et al. (2024) · British Journal of Pharmacology · doi:10.1111/bph.17302 · Cited by 86
Liu, F., Zheng, J., Zheng, L., & Chen, C. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.09.012 · Cited by 83
Kamath, U., Liu, J., & Whitaker, J. (2019) · Springer eBooks · doi:10.1007/978-3-030-14596-5_5 · Cited by 82
Dataset for Identification of Homophobia and Transophobia in Multilingual YouTube CommentsPreprint
Chakravarthi, B. R., Priyadharshini, R., Ponnusamy, R., Kumaresan, P. K., Sampath, K., Thenmozhi, D., Thangasamy, S., Nallathambi, R., & McCrae, J. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.00227 · Cited by 82
Examining Gender Bias in Languages with Grammatical Gender
Zhou, P., Shi, W., Zhao, J., Huang, K.-H., Chen, M., Cotterell, R., & Chang, K.-W. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1531 · Cited by 81
Visual question answering: a state-of-the-art review
Manmadhan, S., & Kovoor, B. C. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09832-7 · Cited by 82
AI-Enabled Automation for Completeness Checking of Privacy Policies
Amaral, O., Abualhaija, S., Torre, D., Sabetzadeh, M., & Briand, L. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3124332 · Cited by 94
Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction
Chen, Z., Huang, H., Liu, B., Shi, X., & Jin, H. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.128 · Cited by 84
Lin, C., Keleş, Ü., & Adolphs, R. (2021) · Nature Communications · doi:10.1038/s41467-021-25500-y · Cited by 87
Ghourabi, A., & Alohaly, M. (2023) · Sensors · doi:10.3390/s23083861 · Cited by 88
Rahman, A., Mahir, S. H., Tashrif, M. T. A., Karim, M. A., Aishi, A. A., Kundu, D., Debnath, T., Moududi, M. A. A., Eidmum, M. Z. A., Miah, A. S. M., et al. (2025) · Systems and Soft Computing · doi:10.1016/j.sasc.2025.200396 · Cited by 101
Robust Lexical Features for Improved Neural Network Named-Entity RecognitionPreprint
Ghaddar, A., & Langlais, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.03489 · Cited by 86
Miura, Y., Taniguchi, M., Taniguchi, T., & Ohkuma, T. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1116 · Cited by 80
Indosum: A New Benchmark Dataset for Indonesian Text Summarization
Kurniawan, K., & Louvan, S. (2018) · International Conference on Asian Language Processing · doi:10.1109/ialp.2018.8629109 · Cited by 86
Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies
Kim, Y., Gao, Y., & Ney, H. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1120 · Cited by 90
Deep Learning-Based Vulnerable Function Detection: A Benchmark
Lin, G., Xiao, W., Zhang, J., & Xiang, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-41579-2_13 · Cited by 81
Hierarchical deep multi-modal network for medical visual question answering
Gupta, D., Suman, S., & Ekbal, A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113993 · Cited by 81
Arabic Aspect-Based Sentiment Analysis: A Systematic Literature Review
Obiedat, R., Al-Darras, D., Alzaghoul, E. F., & Harfoushi, O. (2021) · IEEE Access · doi:10.1109/access.2021.3127140 · Cited by 80
COSMOS: Catching Out-of-Context Image Misuse Using Self-Supervised Learning
Aneja, S., Bregler, C., & Nießner, M. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26648 · Cited by 110
Fraud detection with natural language processing
Boulieris, P., Pavlopoulos, J., Xenos, A., & Vassalos, V. (2023) · Machine Learning · doi:10.1007/s10994-023-06354-5 · Cited by 89
The amorphous state as a frontier in computational materials design
Liu, Y., Madanchi, A., Anker, A. S., Simine, L., & Deringer, V. L. (2024) · Nature Reviews Materials · doi:10.1038/s41578-024-00754-2 · Cited by 97
An Open-World Extension to Knowledge Graph Completion Models
Shah, H., Villmow, J., Ulges, A., Schwanecke, U., & Faisal Shafait, B. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013044 · Cited by 81
Toxic Comment Detection in Online Discussions
Risch, J., & Krestel, R. (2020) · Algorithms for intelligent systems · doi:10.1007/978-981-15-1216-2_4 · Cited by 85
Carosia, A. E. D. O., Coelho, G. P., & da Silva, A. E. A. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115470 · Cited by 83
Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRFPreprint
Shao, Y., Hardmeier, C., Tiedemann, J., & Nivre, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01314 · Cited by 82
Peer Effects in Product Adoption
Bailey, M., Johnston, D., Kuchler, T., Stroebel, J., & Wong, A. (2022) · American Economic Journal Applied Economics · doi:10.1257/app.20200367 · Cited by 80
Kucherenko, T., Jonell, P., Yoon, Y., Wolfert, P., & Henter, G. E. (2021) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3397481.3450692 · Cited by 83
The Four Generations of Entity Resolution
Papadakis, G., Ioannou, E., Thanos, E., & Palpanas, T. (2021) · Synthesis lectures on data management · doi:10.1007/978-3-031-01878-7 · Cited by 78
Industry Scale Semi-Supervised Learning for Natural Language Understanding
Chen, L., Garcia, F., Kumar, V., Xie, H., & Lu, J. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-industry.39 · Cited by 78
Social media crowdsourcing for rapid damage assessment following a sudden-onset natural hazard event
Li, L., Bensi, M., Cui, Q., Baecher, G. B., & Huang, Y. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102378 · Cited by 82
Bloom’s Learning Outcomes’ Automatic Classification Using LSTM and Pretrained Word Embeddings
Shaikh, S., Daudpota, S. M., & Imran, A. S. (2021) · IEEE Access · doi:10.1109/access.2021.3106443 · Cited by 89
CyBERT: Cybersecurity Claim Classification by Fine-Tuning the BERT Language Model
Ameri, K., Hempel, M., Sharif, H., Lopez, J., & Perumalla, K. S. (2021) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp1040031 · Cited by 82
Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine TranslationPreprint
Eriguchi, A., Johnson, M., Fırat, O., Kazawa, H., & Macherey, W. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.04686 · Cited by 86
Recent Advances in Named Entity Recognition: A Comprehensive Survey and Comparative StudyPreprint
Keraghel, I., Morbieu, S., & Nadif, M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.10825 · Cited by 144
Dynamic Embeddings for User Profiling in Twitter
Liang, S., Zhang, X., Ren, Z., & Kanoulas, E. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220043 · Cited by 77
Asgari, E., McHardy, A. C., & Mofrad, M. R. K. (2019) · Scientific Reports · doi:10.1038/s41598-019-38746-w · Cited by 77
Cross-lingual Structure Transfer for Relation and Event Extraction
Subburathinam, A., Lu, D., Ji, H., May, J., Chang, S.-F., Sil, A., & Voss, C. R. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1030 · Cited by 77
Mansouri, B., Rohatgi, S., Oard, D. W., Wu, J. F., Giles, C. L., & Zanibbi, R. (2019) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/3341981.3344235 · Cited by 77
From static to dynamic word representations: a survey
Wang, Y., Hou, Y., Che, W., & Liu, T. (2020) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-020-01069-8 · Cited by 87
Silvestri, S., Islam, S., Papastergiou, S., Tzagkarakis, C., & Ciampi, M. (2023) · Sensors · doi:10.3390/s23020651 · Cited by 76
Hierarchical Neural Networks for Sequential Sentence Classification in Medical Scientific Abstracts
Jin, D., & Szolovits, P. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1349 · Cited by 89
Alshaabi, T., Dewhurst, D. R., Minot, J. R., Arnold, M. V., Adams, J. L., Danforth, C. M., & Dodds, P. S. (2021) · EPJ Data Science · doi:10.1140/epjds/s13688-021-00271-0 · Cited by 92
UTSA: Urdu Text Sentiment Analysis Using Deep Learning Methods
Naqvi, U., Majid, A., & Abbas, S. A. (2021) · IEEE Access · doi:10.1109/access.2021.3104308 · Cited by 76
Metadata
- Authors
- Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T.
- Affiliations
- Meta (United States) (US)
- Facebook AI Research,
- Corresponding author
- Bojanowski, Piotr
- Joulin, Armand
- ORCID
- Bojanowski, Piotr
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press
- Published
- 2017-12
- Volume
- 5
- Pages
- 135-146
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W2493916176
- Cited by
- 9,645
- References
- 47
- Citations by year
- 2026: 389 · 2025: 677 · 2024: 790 · 2023: 1188 · 2022: 1253 · 2021: 1691 · 2020: 1561 · 2019: 1288 · 2018: 648 · 2017: 142
- FWCI
- 593.75
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Transactions of the Association for Computational Linguistics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Keywords
- word embeddings, character n-grams, skip-gram model, out-of-vocabulary words, word similarity, word analogy, multilingual word representation
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
