Scholar
Michael Sejr Schlichtkrull
Google Scholar ID: z8YvWyEAAAAJ
Lecturer, Queen Mary University of London
Citations & Impact
Citations
9,187
H-index
15
i10-index
18
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- He has published multiple papers in various fields including automated fact verification, scientific writing assistance, document-level claim extraction and decontextualization, and more. Specific publications include:
- - Generating Media Background Checks for Automated Source Critical Reasoning
- - Automated Focused Feedback Generation for Scientific Writing Assistance
- - Document-level Claim Extraction and Decontextualisation for Fact-Checking
- - The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who
- - Are Embedded Potatoes Still Vegetables? On the Limitations of WordNet Embeddings for Lexical Semantics
- - AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web
- - A Survey on Automated Fact-Checking
- - UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
- - FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information
- - Joint Verification and Reranking for Open Fact Checking Over Tables
- - Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking
- - NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Research Experience
- Before joining Queen Mary University of London, he was a postdoctoral research associate and affiliated lecturer at the University of Cambridge, working with Andreas Vlachos on automated fact verification. He was also a research associate at Fitzwilliam College.
Education
- Graduated in 2021 with a PhD thesis from the University of Amsterdam, where he worked with Ivan Titov on building NLP models that incorporate structured data.
Background
- His research focuses on automated reasoning (by LLMs and other NLP models) over retrieved evidence, especially for fact-checking and problems with similarly complex epistemology. He is also very interested in modeling structured data sources, such as knowledge graphs, tables, or parse trees. He studies technologies that improve the way we interact with information, whether through question answering systems or fact-checking systems.
Co-authors