---
title: "Who cites Matching the Blanks: Distributional Similarity…"
description: "294 papers of the Elamur Research library cite «Matching the Blanks: Distributional Similarity for Relation Learning», found in their bibliographies; the…"
canonical: "https://research.elamur.ai/free/en/papers/soares-2019-matching-blanks-distributional-similarity-relation/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Matching the Blanks: Distributional Similarity…](https://md.research.elamur.ai/free/en/papers/soares-2019-matching-blanks-distributional-similarity-relation/) / cited by

# Who cites «Matching the Blanks: Distributional Similarity for Relation Learning»

294 papers of our library cite it (of the 140,884 whose bibliographies we read) · 13 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/soares-2019-matching-blanks-distributional-similarity-relation/)

1. [**Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing**Yu Gu; Robert Tinn; Hao Cheng; Michael Lucas; Naoto Usuyama; Xiaodong Liu; Tristan Naumann; Jianfeng Gao;, Hoifung Poon*2020 · 2,636 citations*](https://md.research.elamur.ai/free/en/papers/gu-2020-domain-specific-language-model-pretraining/)
2. [**SpanBERT: Improving Pre-training by Representing and Predicting Spans**Mandar Joshi, Danqi Chen, Yinhan Liu, et al.*2019 · 2,580 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/)
3. [**Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,548 citations*](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/)
4. [**Unifying Large Language Models and Knowledge Graphs: A Roadmap**Shirui Pan, Linhao Luo, Yufei Wang, et al.*2023 · 1,545 citations*](https://md.research.elamur.ai/free/en/papers/pan-2023-unifying-large-language-models-knowledge/)
5. [**How Can We Know What Language Models Know?**Zhengbao Jiang, Frank F. Xu, Jun Araki, et al.*2020 · 1,493 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2020-how-can-we-know-what/)
6. [**K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters**Ruize Wang, Duyu Tang, Nan Duan, et al.*2020 · 662 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-k-adapter-infusing-knowledge-pre/)
7. [**A Frustratingly Easy Approach for Entity and Relation Extraction**Zexuan Zhong, Danqi Chen*2020 · 629 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2020-frustratingly-easy-approach-entity-relation/)
8. [**GPT-RE: In-context Learning for Relation Extraction using Large Language Models**Zhen Wan, Fei Cheng, Zhuoyuan Mao, et al.*2023 · 252 citations*](https://md.research.elamur.ai/free/en/papers/wan-2023-gpt-re-context-learning-relation/)
9. [**TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task**Christoph Alt, Aleksandra Gabryszak, Leonhard Hennig*2020 · 212 citations*](https://md.research.elamur.ai/free/en/papers/alt-2020-tacred-revisited-thorough-evaluation-tacred/)
10. [**Entities as Experts: Sparse Memory Access with Entity Supervision**Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, et al.*2020 · 205 citations*](https://md.research.elamur.ai/free/en/papers/fevry-2020-entities-as-experts-sparse-memory/)
11. [**Temporal Relation Classification in Hebrew**Guy Yanko, Shahaf Pariente, Kfir Bar*2023 · 2 citations*](https://md.research.elamur.ai/free/en/papers/yanko-2023-temporal-relation-classification-hebrew/)
12. [**A Study on Building Efficient Zero-Shot Relation Extraction Models**Hugo THOMAS, Caio Corro, Guillaume Gravier, et al.*2026*](https://md.research.elamur.ai/free/en/papers/thomas-2026-study-building-efficient-zero-shot/)
13. [**Contrastively Pre-trained Event Embeddings with Schema-free LLM Annotations**Frank Mtumbuka, Steven Schockaert*2026*](https://md.research.elamur.ai/free/en/papers/mtumbuka-2026-contrastively-pre-trained-event-embeddings/)

281 more papers of our library cite it; they have no page here yet.

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