K. Erk
Natural Language Processing · with Padó · 2007–2020
A researcher in the Science corpus: 8 papers; trusts 95 researchers and is trusted by 227 researchers here, as inferred from the citation graph.
Subfields
- Natural Language Processing — 71 citations into it; #352 of 5,688 by trust here, #915 by citations
- Artificial Intelligence — 47 citations into it; #3,043 of 35,345 by trust here, #8,999 by citations
- Representation & Self-Supervised Learning — 22 citations into it; #482 of 3,145 by trust here, #1,395 by citations
- Computational Linguistics & Semantics — 12 citations into it; #32 of 278 by trust here, #140 by citations
- Developmental and Educational Psychology — 9 citations into it
- Evaluation & Benchmarks — 8 citations into it; #294 of 3,147 by trust here, #888 by citations
- Information Retrieval & Search — 8 citations into it
- Cognitive Science & Psychology — 7 citations into it
Whom K. Erk trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- T. Landauer
- Hinrich Schütze
- Dekang Lin
- Philip Resnik
- Scott McDonald
- Geoffrey E. Hinton
- Jeff Mitchell
- Mirella Lapata
- Sebastian Padó
- Walter Kintsch
Who trusts K. Erk overall
- Alessandro Lenci
- Aurélie Herbelot
- Marco Baroni
- José Camacho-Collados
- Yonatan Belinkov
- Peter D. Turney
- Felix Stahlberg
- Edward Grefenstette
- Karl Moritz Hermann
- Richard Socher
Papers from K. Erk's perspective
Ordered by how strongly the researchers K. Erk trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- A Structured Vector Space Model for Word Meaning in Context (2008) — 405 citations
- Vector Space Models of Word Meaning and Phrase Meaning: A Survey (2012) — 340 citations
- How to marry a star: Probabilistic constraints for meaning in context (2020) — 8 citations
- Representing words as regions in vector space (2009) — 66 citations
- What Substitutes Tell Us - Analysis of an “All-Words” Lexical Substitution Corpus (2014) — 98 citations
- Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2016) — 1,401 citations
- Word Sense Clustering and Clusterability (2016) — 58 citations
- A Simple, Similarity-based Model for Selectional Preferences (2007) — 161 citations