---
title: "Who cites Efficient Estimation of Word — 3,933 citing papers"
description: "3,933 papers of the Elamur Research library cite «Efficient Estimation of Word Representations in Vector Space», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Efficient Estimation of Word Representations in…](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/) / cited by

# Who cites «Efficient Estimation of Word Representations in Vector Space»

3,933 papers of our library cite it (of the 141,840 whose bibliographies we read) · 344 with a page here, the most cited first · page 1 of 4 · 1–100 · [← back to «Efficient Estimation of Word Representations in Vector Space»](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/)

**1** [2](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/cited-by/page/2/) [3](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/cited-by/page/3/) [4](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/cited-by/page/4/) [next →](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/cited-by/page/2/)

1. **[GloVe: Global Vectors for Word Representation](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jeffrey Pennington, Richard Socher, Christopher Manning*2014 · 38,574 citations*
2. [**Distributed Representations of Words and Phrases and their Compositionality arXiv:1310.4546**Tomas Mikolov, Ilya Sutskever, Kai Chen, et al.*2013 · 21,491 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2013-distributed-representations-words-phrases-their/)
3. [**Language Models are Few-Shot Learners arXiv:2005.14165**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 13,703 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
4. **[BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,784 citations*
5. **[Enriching Word Vectors with Subword Information](https://md.research.elamur.ai/free/en/papers/bojanowski-2017-enriching-word-vectors-subword-information/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Piotr Bojanowski, Edouard Grave, Armand Joulin, et al.*2017 · 11,848 citations*
6. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,088 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
7. [**TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems arXiv:1603.04467**Martín Abadi, Ashish Agarwal, Paul Barham, et al.*2016 · 9,464 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-large-scale-machine-learning/)
8. [**A Simple Framework for Contrastive Learning of Visual Representations arXiv:2002.05709**Ting Chen, Simon Kornblith, Mohammad Norouzi, et al.*2020 · 7,783 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-simple-framework-contrastive-learning-visual/)
9. [**Representation Learning with Contrastive Predictive Coding arXiv:1807.03748**Aaron van den Oord, Yazhe Li, Oriol Vinyals*2018 · 6,842 citations*](https://md.research.elamur.ai/free/en/papers/oord-2018-representation-learning-contrastive-predictive-coding/)
10. [**Intriguing properties of neural networks arXiv:1312.6199**Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, et al.*2013 · 5,878 citations*](https://md.research.elamur.ai/free/en/papers/szegedy-2013-intriguing-properties-neural-networks/)
11. [**Distributed Representations of Sentences and Documents arXiv:1405.4053**Quoc V. Le, Tomas Mikolov*2014 · 5,786 citations*](https://md.research.elamur.ai/free/en/papers/le-2014-distributed-representations-sentences-documents/)
12. **[Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://md.research.elamur.ai/free/en/papers/krishna-2016-visual-genome-connecting-language-vision/) [International Journal of Computer Vision](https://md.research.elamur.ai/free/en/venues/international-journal-of-computer-vision/)**Ranjay Krishna, Yuke Zhu, Oliver Groth, et al.*2016 · 5,761 citations*
13. **[Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,396 citations*
14. **[Neural Architectures for Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/lample-2016-neural-architectures-named-entity-recognition/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, et al.*2016 · 5,311 citations*
15. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,056 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
16. [**Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books arXiv:1506.06724**Yukun Zhu, Ryan Kiros, Richard Zemel, et al.*2015 · 3,051 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2015-aligning-books-movies-towards-story/)
17. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,863 citations*
18. [**Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing arXiv:2007.15779**Yu Gu; Robert Tinn; Hao Cheng; Michael Lucas; Naoto Usuyama; Xiaodong Liu; Tristan Naumann; Jianfeng Gao;, Hoifung Poon*2020 · 2,655 citations*](https://md.research.elamur.ai/free/en/papers/gu-2020-domain-specific-language-model-pretraining/)
19. [**Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings arXiv:1607.06520**Tolga Bolukbasi, Kai-Wei Chang, James Zou, et al.*2016 · 2,050 citations*](https://md.research.elamur.ai/free/en/papers/bolukbasi-2016-man-computer-programmer-woman-homemaker/)
20. **[Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/hogan-2020-knowledge-graphs/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Aidan Hogan, Eva Blomqvist, Michael Cochez, et al.*2020 · 2,013 citations*
21. **[Character-Aware Neural Language Models](https://md.research.elamur.ai/free/en/papers/kim-2015-character-aware-neural-language-models/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yoon Kim, Yacine Jernite, David Sontag, et al.*2015 · 1,860 citations*
22. **[SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation](https://md.research.elamur.ai/free/en/papers/cer-2017-semeval-2017-task-1-semantic/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Daniel Cer, Mona Diab, Eneko Agirre, et al.*2017 · 1,813 citations*
23. **[A Survey on Hate Speech Detection using Natural Language Processing](https://md.research.elamur.ai/free/en/papers/schmidt-2017-survey-hate-speech-detection-natural/) [International Workshop on Natural Language Processing for Social Media](https://md.research.elamur.ai/free/en/venues/socialnlp/)**Anna Schmidt, Michael Wiegand*2017 · 1,805 citations*
24. [**A Structured Self-attentive Sentence Embedding arXiv:1703.03130**Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, et al.*2017 · 1,782 citations*](https://md.research.elamur.ai/free/en/papers/lin-2017-structured-self-attentive-sentence-embedding/)
25. **[Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors](https://md.research.elamur.ai/free/en/papers/baroni-2014-don-t-count-predict-systematic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Marco Baroni, Georgiana Dinu, Germán Kruszewski*2014 · 1,690 citations*
26. **[SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation](https://md.research.elamur.ai/free/en/papers/hill-2015-simlex-999-evaluating-semantic-models/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Felix Hill, Roi Reichart, Anna Korhonen*2015 · 1,629 citations*
27. **[Improving Distributional Similarity with Lessons Learned from Word Embeddings](https://md.research.elamur.ai/free/en/papers/levy-2015-improving-distributional-similarity-lessons-learned/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Omer Levy, Yoav Goldberg, Ido Dagan*2015 · 1,624 citations*
28. [**Layer Normalization arXiv:1607.06450**Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton*2016 · 1,618 citations*](https://md.research.elamur.ai/free/en/papers/ba-2016-layer-normalization/)
29. **[Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://md.research.elamur.ai/free/en/papers/jia-2021-scaling-up-visual-vision-language/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Chao Jia, Yinfei Yang, Ye Xia, et al.*2021 · 1,537 citations*
30. **[The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://md.research.elamur.ai/free/en/papers/joshi-2020-state-fate-linguistic-diversity-inclusion/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pratik Joshi, Sebastin Santy, Amar Budhiraja, et al.*2020 · 1,494 citations*
31. [**Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models arXiv:1411.2539**Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel*2014 · 1,325 citations*](https://md.research.elamur.ai/free/en/papers/kiros-2014-unifying-visual-semantic-embeddings-multimodal/)
32. **[Latent Retrieval for Weakly Supervised Open Domain Question Answering](https://md.research.elamur.ai/free/en/papers/lee-2019-latent-retrieval-weakly-supervised-open/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kenton Lee, Ming-Wei Chang, Kristina Toutanova*2019 · 1,236 citations*
33. **[Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/chen-2015-event-extraction-dynamic-multi-pooling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yubo Chen, Liheng Xu, Kang Liu, et al.*2015 · 1,175 citations*
34. **[Recurrent Attention Network on Memory for Aspect Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/chen-2017-recurrent-attention-network-memory-aspect/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Peng Chen, Zhongqian Sun, Lidong Bing, et al.*2017 · 1,117 citations*
35. **[Context-Dependent Sentiment Analysis in User-Generated Videos](https://md.research.elamur.ai/free/en/papers/poria-2017-context-dependent-sentiment-analysis-user/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Soujanya Poria, Erik Cambria, Devamanyu Hazarika, et al.*2017 · 1,027 citations*
36. [**VL-BERT: Pre-training of Generic Visual-Linguistic Representations arXiv:1908.08530**Weijie Su, Xizhou Zhu, Yue Cao, et al.*2019 · 1,025 citations*](https://md.research.elamur.ai/free/en/papers/su-2019-vl-bert-pre-training-generic/)
37. **[On Using Very Large Target Vocabulary for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/jean-2015-very-large-target-vocabulary-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sébastien Jean, Kyunghyun Cho, Roland Memisevic, et al.*2015 · 1,005 citations*
38. **[ERNIE 2.0: A Continual Pre-training Framework for Language Understanding](https://md.research.elamur.ai/free/en/papers/sun-2019-ernie-2-0-continual/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yu Sun, Shuohuan Wang, Yukun Li, et al.*2019 · 984 citations*
39. [**GPQA: A Graduate-Level Google-Proof Q&A Benchmark arXiv:2311.12022**David Rein, Betty Li Hou, Asa Cooper Stickland, et al.*2023 · 964 citations*](https://md.research.elamur.ai/free/en/papers/rein-2023-gpqa-graduate-level-google-proof/)
40. [**ERNIE: Enhanced Representation through Knowledge Integration arXiv:1904.09223**Yu Sun, Shuohuan Wang, Yukun Li, et al.*2019 · 958 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-ernie-enhanced-representation-through-knowledge/)
41. **[Using the Output Embedding to Improve Language Models](https://md.research.elamur.ai/free/en/papers/press-2017-output-embedding-improve-language-models/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Ofir Press, Lior Wolf*2017 · 897 citations*
42. **[Joint Event Extraction via Recurrent Neural Networks](https://md.research.elamur.ai/free/en/papers/nguyen-2016-joint-event-extraction-recurrent-neural/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Thien Huu Nguyen, Kyunghyun Cho, Ralph Grishman*2016 · 882 citations*
43. [**Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers arXiv:1801.04354**Ji Gao, Jack Lanchantin, Mary Lou Soffa, et al.*2018 · 838 citations*](https://md.research.elamur.ai/free/en/papers/gao-2018-black-box-generation-adversarial-text/)
44. **[SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation](https://md.research.elamur.ai/free/en/papers/agirre-2016-semeval-2016-task-1-semantic/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Eneko Agirre, Carmen Banea, Daniel Cer, et al.*2016 · 824 citations*
45. **[Improving Vector Space Word Representations Using Multilingual Correlation](https://md.research.elamur.ai/free/en/papers/faruqui-2014-improving-vector-space-word-representations/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Manaal Faruqui, Chris Dyer*2014 · 822 citations*
46. **[Linguistic Regularities in Sparse and Explicit Word Representations](https://md.research.elamur.ai/free/en/papers/levy-2014-linguistic-regularities-sparse-explicit-word/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Omer Levy, Yoav Goldberg*2014 · 796 citations*
47. **[SemEval-2015 Task 2: Semantic Textual Similarity, English, Spanish and Pilot on Interpretability](https://md.research.elamur.ai/free/en/papers/agirre-2015-semeval-2015-task-2-semantic/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Eneko Agirre, Carmen Banea, Claire Cardie, et al.*2015 · 763 citations*
48. **[Learning Distributed Representations of Sentences from Unlabelled Data](https://md.research.elamur.ai/free/en/papers/hill-2016-learning-distributed-representations-sentences-unlabelled/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Felix Hill, Kyunghyun Cho, Anna Korhonen*2016 · 756 citations*
49. **[IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages](https://md.research.elamur.ai/free/en/papers/kakwani-2020-indicnlpsuite-monolingual-corpora-evaluation-benchmarks/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, et al.*2020 · 730 citations*
50. **[Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/rajani-2019-explain-yourself-leveraging-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, et al.*2019 · 693 citations*
51. **[Bilingual Word Embeddings for Phrase-Based Machine Translation](https://md.research.elamur.ai/free/en/papers/zou-2013-bilingual-word-embeddings-phrase-based/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Will Y. Zou, Richard Socher, Daniel Cer, et al.*2013 · 685 citations*
52. **[Semantic Parsing via Paraphrasing](https://md.research.elamur.ai/free/en/papers/berant-2014-semantic-parsing-paraphrasing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jonathan Berant, Percy Liang*2014 · 682 citations*
53. **[context2vec: Learning Generic Context Embedding with Bidirectional LSTM](https://md.research.elamur.ai/free/en/papers/melamud-2016-context2vec-learning-generic-context-embedding/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Oren Melamud, Jacob Goldberger, Ido Dagan*2016 · 672 citations*
54. **[Using Convolutional Neural Networks to Classify Hate-Speech](https://md.research.elamur.ai/free/en/papers/gamback-2017-convolutional-neural-networks-classify-hate/) [Workshop on Abusive Language Online](https://md.research.elamur.ai/free/en/venues/workshop-on-abusive-language-online/)**Björn Gambäck, Utpal Kumar Sikdar*2017 · 651 citations*
55. **[Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging](https://md.research.elamur.ai/free/en/papers/reimers-2017-reporting-score-distributions-makes-difference/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nils Reimers, Iryna Gurevych*2017 · 650 citations*
56. [**Show and Tell: A Neural Image Caption Generator arXiv:1411.4555**Oriol Vinyals, Alexander Toshev, Samy Bengio, et al.*2014 · 645 citations*](https://md.research.elamur.ai/free/en/papers/vinyals-2014-show-tell-neural-image-caption/)
57. **[Persistent Anti-Muslim Bias in Large Language Models](https://md.research.elamur.ai/free/en/papers/abid-2021-persistent-anti-muslim-bias-large/) [Proceedings of the AAAI/ACM Conference on AI Ethics and Society](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-acm-conference-on-ai-ethics-and-society/)**Abubakar Abid, Maheen Farooqi, James Zou*2021 · 644 citations*
58. [**A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)**Alane Suhr, Stephanie Zhou, Ally Zhang, et al.*2019 · 644 citations*](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/)
59. **[Learning Word Vectors for 157 Languages](https://md.research.elamur.ai/free/en/papers/grave-2018-learning-word-vectors-157-languages/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Edouard Grave, Piotr Bojanowski, Prakhar Gupta, et al.*2018 · 641 citations*
60. **[Advances in Pre-Training Distributed Word Representations](https://md.research.elamur.ai/free/en/papers/mikolov-2017-advances-pre-training-distributed-word/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Tomas Mikolov, Edouard Grave, Piotr Bojanowski, et al.*2017 · 617 citations*
61. **[Classifying Relations by Ranking with Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/santos-2015-classifying-relations-ranking-convolutional-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cícero dos Santos, Bing Xiang, Bowen Zhou*2015 · 616 citations*
62. **[Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space](https://md.research.elamur.ai/free/en/papers/neelakantan-2014-efficient-non-parametric-estimation-multiple/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Arvind Neelakantan, Jeevan Shankar, Alexandre Passos, et al.*2014 · 597 citations*
63. [**Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces arXiv:1805.10190**Alice Coucke, Alaa Saade, Adrien Ball, et al.*2018 · 595 citations*](https://md.research.elamur.ai/free/en/papers/coucke-2018-snips-voice-platform-embedded-spoken/)
64. [**Text Embeddings by Weakly-Supervised Contrastive Pre-training arXiv:2212.03533**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2022 · 594 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-text-embeddings-weakly-supervised-contrastive/)
65. **[WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations](https://md.research.elamur.ai/free/en/papers/pilehvar-2019-wic-word-context-dataset-evaluating/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Mohammad Taher Pilehvar, Jose Camacho-Collados*2019 · 594 citations*
66. [**Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks arXiv:1608.04207**Yossi Adi, Einat Kermany, Yonatan Belinkov, et al.*2016 · 591 citations*](https://md.research.elamur.ai/free/en/papers/adi-2016-fine-grained-analysis-sentence-embeddings/)
67. **[Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings](https://md.research.elamur.ai/free/en/papers/liu-2015-fine-grained-opinion-mining-recurrent/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Pengfei Liu, Shafiq Joty, Helen Meng*2015 · 590 citations*
68. **[Dissecting Contextual Word Embeddings: Architecture and Representation](https://md.research.elamur.ai/free/en/papers/peters-2018-dissecting-contextual-word-embeddings-architecture/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Matthew E. Peters, Mark Neumann, Luke Zettlemoyer, et al.*2018 · 587 citations*
69. **[Relation Classification via Multi-Level Attention CNNs](https://md.research.elamur.ai/free/en/papers/wang-2016-relation-classification-multi-level-attention/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Linlin Wang, Zhu Cao, Gerard de Melo, et al.*2016 · 576 citations*
70. [**An efficient framework for learning sentence representations arXiv:1803.02893**Lajanugen Logeswaran, Honglak Lee*2018 · 575 citations*](https://md.research.elamur.ai/free/en/papers/logeswaran-2018-efficient-framework-learning-sentence-representations/)
71. **[A Survey on Recent Advances in Named Entity Recognition from Deep Learning models](https://md.research.elamur.ai/free/en/papers/yadav-2018-survey-recent-advances-named-entity/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Vikas Yadav, Steven Bethard*2018 · 569 citations*
72. **[Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://md.research.elamur.ai/free/en/papers/rothe-2019-leveraging-pre-trained-checkpoints-sequence/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Sascha Rothe, Shashi Narayan, Aliaksei Severyn*2019 · 563 citations*
73. **[Learning principled bilingual mappings of word embeddings while preserving monolingual invariance](https://md.research.elamur.ai/free/en/papers/artetxe-2016-learning-principled-bilingual-mappings-word/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mikel Artetxe, Gorka Labaka, Eneko Agirre*2016 · 535 citations*
74. [**The Natural Language Decathlon: Multitask Learning as Question Answering arXiv:1806.08730**Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al.*2018 · 529 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
75. **[Normalized Word Embedding and Orthogonal Transform for Bilingual Word Translation](https://md.research.elamur.ai/free/en/papers/xing-2015-normalized-word-embedding-orthogonal-transform/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chao Xing, Dong Wang, Chao Liu, et al.*2015 · 528 citations*
76. **[The United Nations Parallel Corpus v1.0](https://md.research.elamur.ai/free/en/papers/ziemski-2016-united-nations-parallel-corpus-v1/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Michał Ziemski, Marcin Junczys-Dowmunt, Bruno Pouliquen*2016 · 525 citations*
77. **[Stance Detection with Bidirectional Conditional Encoding](https://md.research.elamur.ai/free/en/papers/augenstein-2016-stance-detection-bidirectional-conditional-encoding/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, et al.*2016 · 522 citations*
78. **[How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://md.research.elamur.ai/free/en/papers/zhong-2020-how-does-nlp-benefit-legal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoxi Zhong, Chaojun Xiao, Cunchao Tu, et al.*2020 · 518 citations*
79. **[Recursive Neural Conditional Random Fields for Aspect-based Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/wang-2016-recursive-neural-conditional-random-fields/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, et al.*2016 · 515 citations*
80. **[Learning Gender-Neutral Word Embeddings](https://md.research.elamur.ai/free/en/papers/zhao-2018-learning-gender-neutral-word-embeddings/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jieyu Zhao, Yichao Zhou, Zeyu Li, et al.*2018 · 514 citations*
81. **[The LAMBADA dataset: Word prediction requiring a broad discourse context](https://md.research.elamur.ai/free/en/papers/paperno-2016-lambada-dataset-word-prediction-requiring/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, et al.*2016 · 511 citations*
82. [**Learned in Translation: Contextualized Word Vectors arXiv:1708.00107**Bryan McCann, James Bradbury, Caiming Xiong, et al.*2017 · 496 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2017-learned-translation-contextualized-word-vectors/)
83. [**Word Sense Disambiguation: A Unified Evaluation Framework and Empirical Comparison**Alessandro Raganato, Jose Camacho-Collados, Roberto Navigli*2017 · 484 citations*](https://md.research.elamur.ai/free/en/papers/raganato-2017-word-sense-disambiguation-unified-evaluation/)
84. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts arXiv:2112.06905**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 479 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
85. **[Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion](https://md.research.elamur.ai/free/en/papers/joulin-2018-loss-translation-learning-bilingual-word/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Armand Joulin, Piotr Bojanowski, Tomas Mikolov, et al.*2018 · 477 citations*
86. [**Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT arXiv:1904.09077**Shijie Wu, Mark Dredze*2019 · 468 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-beto-bentz-becas-surprising-cross/)
87. **[Emergent: a novel data-set for stance classification](https://md.research.elamur.ai/free/en/papers/ferreira-2016-emergent-novel-data-set-stance/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**William Ferreira, Andreas Vlachos*2016 · 458 citations*
88. **[Knowledge Graph and Text Jointly Embedding](https://md.research.elamur.ai/free/en/papers/wang-2014-knowledge-graph-text-jointly-embedding/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Wang, Jianwen Zhang, Jianlin Feng, et al.*2014 · 457 citations*
89. **[Bilingual Word Representations with Monolingual Quality in Mind](https://md.research.elamur.ai/free/en/papers/luong-2015-bilingual-word-representations-monolingual-quality/) [Workshop on Vector Space Modeling for Natural Language Processing](https://md.research.elamur.ai/free/en/venues/workshop-on-vector-space-modeling-for-natural-language-processing/)**Thang Luong, Hieu Pham, Christopher D. Manning*2015 · 454 citations*
90. **[Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/he-2015-multi-perspective-sentence-similarity-modeling/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hua He, Kevin Gimpel, Jimmy Lin*2015 · 450 citations*
91. **[CARER: Contextualized Affect Representations for Emotion Recognition](https://md.research.elamur.ai/free/en/papers/saravia-2018-carer-contextualized-affect-representations-emotion/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Elvis Saravia, Hsien-Chi Toby Liu, Yen-Hao Huang, et al.*2018 · 448 citations*
92. **[Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings](https://md.research.elamur.ai/free/en/papers/song-2018-directional-skip-gram-explicitly-distinguishing/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yan Song, Shuming Shi, Jing Li, et al.*2018 · 442 citations*
93. **[Temporal Analysis of Language through Neural Language Models](https://md.research.elamur.ai/free/en/papers/kim-2014-temporal-analysis-language-through-neural/) [ACL Workshop on Language Technologies and Computational Social Science](https://md.research.elamur.ai/free/en/venues/acl-workshop-on-language-technologies-and-computational-social-science/)**Yoon Kim, Yi-I Chiu, Kentaro Hanaki, et al.*2014 · 439 citations*
94. [**The Falcon Series of Open Language Models arXiv:2311.16867**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 436 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/)
95. **[Improving Text Embeddings with Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-improving-text-embeddings-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2024 · 433 citations*
96. **[Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation](https://md.research.elamur.ai/free/en/papers/yamada-2016-joint-learning-embedding-words-entities/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, et al.*2016 · 427 citations*
97. **[Tailoring Continuous Word Representations for Dependency Parsing](https://md.research.elamur.ai/free/en/papers/bansal-2014-tailoring-continuous-word-representations-dependency/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mohit Bansal, Kevin Gimpel, Karen Livescu*2014 · 419 citations*
98. [**PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification**Ellie Pavlick, Pushpendre Rastogi, Juri Ganitkevitch, et al.*2015 · 414 citations*](https://md.research.elamur.ai/free/en/papers/pavlick-2015-ppdb-2-0-better-paraphrase/)
99. **[Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network](https://md.research.elamur.ai/free/en/papers/zhou-2018-multi-turn-response-selection-chatbots/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiangyang Zhou, Lu Li, Daxiang Dong, et al.*2018 · 410 citations*
100. **[Generating Sentences by Editing Prototypes](https://md.research.elamur.ai/free/en/papers/guu-2017-generating-sentences-editing-prototypes/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, et al.*2017 · 407 citations*

**1** [2](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/cited-by/page/2/) [3](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/cited-by/page/3/) [4](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/cited-by/page/4/) [next →](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/cited-by/page/2/)

- [← back to](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/) — Efficient Estimation of Word Representations in Vector Space