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--- nlp:relation_extraction [2021/05/19 11:57] – [Overviews] jmflanig
+++ nlp:relation_extraction [2023/11/23 00:22] (current) – [Papers] jmflanig
@@ Line 8: / Line 8: @@
 ===== Papers =====
   * [[https://arxiv.org/pdf/1601.00770.pdf|Miwa & Bansal 2016 - End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures]]
+  * [[https://arxiv.org/pdf/1708.03743.pdf|Peng et al 2017 - Cross-Sentence N-ary Relation Extraction with Graph LSTMs]]
+  * With LLMs
+    * [[https://arxiv.org/pdf/2305.02105.pdf|Wan et al 2023 - GPT-RE: In-context Learning for Relation Extraction using Large Language Models]]
 ===== OpenIE =====
 For an overview see section 6 of [[https://arxiv.org/pdf/1712.05191.pdf|this paper]].
-  * [[https://www.aaai.org/Papers/IJCAI/2007/IJCAI07-429.pdf|Banko et al 2007 - Open Information Extraction from the Web]] The paper that introduced the task of OpenIE.  Their system was called TextRunner.
+  * [[https://www.aaai.org/Papers/IJCAI/2007/IJCAI07-429.pdf|Banko et al 2007 - Open Information Extraction from the Web]] The paper that introduced the task of OpenIE.  Their system was called TextRunner.  Precurser to [[https://en.wikipedia.org/wiki/Never-Ending_Language_Learning|NELL]]?  Their design is very similar, see [[https://dl.acm.org/doi/pdf/10.1145/3191513|this paper]].  NELL's learning method (and knowledge integrator) is similar to TextRunner's 3 modules (and redundancy-based assessor).
   * [[https://www.aclweb.org/anthology/N19-1309.pdf|Lockard et al 2019 - OpenCeres: When Open Information Extraction Meets the Semi-Structured Web]]