---
title: "Who cites Preemptive Information Extraction — 76 citing papers"
description: "76 papers of the Elamur Research library cite «Preemptive Information Extraction using Unrestricted Relation Discovery», found in their…"
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---
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# Who cites «Preemptive Information Extraction using Unrestricted Relation Discovery»

76 papers of our library cite it (of the 141,840 whose bibliographies we read) · 4 with a page here, the most cited first · [← back to «Preemptive Information Extraction using Unrestricted Relation Discovery»](https://md.research.elamur.ai/free/en/papers/shinyama-2006-preemptive-information-extraction-unrestricted-relation/)

1. [**Open Information Extraction from the Web IJCAI 2007**Michele Banko, Michael J. Cafarella, Stephen Soderland, et al.*2007 · 1,459 citations*](https://md.research.elamur.ai/free/en/papers/banko-2007-open-information-extraction-web/) **[Zero-Shot Transfer Learning for Event Extraction](https://md.research.elamur.ai/free/en/papers/huang-2018-zero-shot-transfer-learning-event/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lifu Huang, Heng Ji, Kyunghyun Cho, et al.*2018 · 272 citations* **[Continual Relation Learning via Episodic Memory Activation and Reconsolidation](https://md.research.elamur.ai/free/en/papers/han-2020-continual-relation-learning-episodic-memory/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xu Han, Yi Dai, Tianyu Gao, et al.*2020 · 151 citations* [**Paraphrasing vs Coreferring: Two Sides of the Same Coin arXiv:2004.14979**Yehudit Meged, Avi Caciularu, Vered Shwartz, et al.*2020 · 47 citations*](https://md.research.elamur.ai/free/en/papers/meged-2020-paraphrasing-vs-coreferring-two-sides/)

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

- [← back to](https://md.research.elamur.ai/free/en/papers/shinyama-2006-preemptive-information-extraction-unrestricted-relation/) — Preemptive Information Extraction using Unrestricted Relation Discovery