--- title: "Who cites Improving Distributional Similarity with Lessons…" description: "313 papers of the Elamur Research library cite «Improving Distributional Similarity with Lessons Learned from Word Embeddings», found in their…" canonical: "https://research.elamur.ai/free/en/papers/levy-2015-improving-distributional-similarity-lessons-learned/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/levy-2015-improving-distributional-similarity-lessons-learned/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Improving Distributional Similarity with Lessons…](https://md.research.elamur.ai/free/en/papers/levy-2015-improving-distributional-similarity-lessons-learned/) / cited by # Who cites «Improving Distributional Similarity with Lessons Learned from Word Embeddings» 313 papers of our library cite it (of the 140,884 whose bibliographies we read) · 14 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/levy-2015-improving-distributional-similarity-lessons-learned/) 1. [**Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change**William L. Hamilton, Jure Leskovec, Dan Jurafsky*2016 · 1,270 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2016-diachronic-word-embeddings-reveal-statistical/) 2. [**Learning Distributed Representations of Sentences from Unlabelled Data**Felix Hill, Kyunghyun Cho, Anna Korhonen*2016 · 756 citations*](https://md.research.elamur.ai/free/en/papers/hill-2016-learning-distributed-representations-sentences-unlabelled/) 3. [**Evaluation methods for unsupervised word embeddings**Tobias Schnabel, Igor Labutov, David Mimno, et al.*2015 · 669 citations*](https://md.research.elamur.ai/free/en/papers/schnabel-2015-evaluation-methods-unsupervised-word-embeddings/) 4. [**Advances in Pre-Training Distributed Word Representations**Tomas Mikolov, Edouard Grave, Piotr Bojanowski, et al.*2017 · 614 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2017-advances-pre-training-distributed-word/) 5. [**Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks**Yossi Adi, Einat Kermany, Yonatan Belinkov, et al.*2016 · 587 citations*](https://md.research.elamur.ai/free/en/papers/adi-2016-fine-grained-analysis-sentence-embeddings/) 6. [**Learning Gender-Neutral Word Embeddings**Jieyu Zhao, Yichao Zhou, Zeyu Li, et al.*2018 · 512 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2018-learning-gender-neutral-word-embeddings/) 7. [**Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora**William L. Hamilton, Kevin Clark, Jure Leskovec, et al.*2016 · 431 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2016-inducing-domain-specific-sentiment-lexicons/) 8. [**Problems With Evaluation of Word Embeddings Using Word Similarity Tasks**Manaal Faruqui, Yulia Tsvetkov, Pushpendre Rastogi, et al.*2016 · 332 citations*](https://md.research.elamur.ai/free/en/papers/faruqui-2016-problems-evaluation-word-embeddings-word/) 9. [**Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn't.**Anna Gladkova, Aleksandr Drozd, Satoshi Matsuoka*2016 · 311 citations*](https://md.research.elamur.ai/free/en/papers/gladkova-2016-analogy-based-detection-morphological-semantic/) 10. [**Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Little**Koustuv Sinha, Robin Jia, Dieuwke Hupkes, et al.*2021 · 292 citations*](https://md.research.elamur.ai/free/en/papers/sinha-2021-masked-language-modeling-distributional-hypothesis/) 11. [**Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings**Rishi Bommasani, Kelly Davis, Claire Cardie*2020 · 248 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2020-interpreting-pretrained-contextualized-representations-reductions/) 12. [**Outta Control: Laws of Semantic Change and Inherent Biases in Word Representation Models**Haim Dubossarsky, Daphna Weinshall, Eitan Grossman*2017 · 196 citations*](https://md.research.elamur.ai/free/en/papers/dubossarsky-2017-outta-control-laws-semantic-change/) 13. [**Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words**Debanjan Ghosh, Weiwei Guo, Smaranda Muresan*2015 · 141 citations*](https://md.research.elamur.ai/free/en/papers/ghosh-2015-sarcastic-or-not-word-embeddings/) 14. [**Hebrew Diacritics Restoration using Visual Representation**Yair Elboher, Yuval Pinter*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/elboher-2026-hebrew-diacritics-restoration-visual-representation/) 299 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/levy-2015-improving-distributional-similarity-lessons-learned/) — Improving Distributional Similarity with Lessons Learned from Word Embeddings