Search Results for author:
Found 7 papers, 2 papers with code
REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE).
Ranked #1 on
Coreference Resolution
on DWIE
Coreference Resolution
Document-level Closed Information Extraction
+7
WebIE: Faithful and Robust Information Extraction on the Web
We evaluate the in-domain, out-of-domain, and zero-shot cross-lingual performance of generative IE models and find models trained on WebIE show better generalisability.
ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking
The model is capable of generalising to large-scale knowledge bases such as Wikidata (which has 15 times more entities than Wikipedia) and of zero-shot entity linking.
Ranked #1 on
Entity Linking
on WebQSP-WD
(using extra training data)
Improving Entity Disambiguation by Reasoning over a Knowledge Base
Recent work in entity disambiguation (ED) has typically neglected structured knowledge base (KB) facts, and instead relied on a limited subset of KB information, such as entity descriptions or types.
Ranked #1 on
Entity Disambiguation
on ShadowLink-Top
DARE: Data Augmented Relation Extraction with GPT-2
Real-world Relation Extraction (RE) tasks are challenging to deal with, either due to limited training data or class imbalance issues.
Deep Bidirectional Transformers for Relation Extraction without Supervision
We present a novel framework to deal with relation extraction tasks in cases where there is complete lack of supervision, either in the form of gold annotations, or relations from a knowledge base.
Reasoning Over Paths via Knowledge Base Completion
We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph.
