Simon Suster

Brief Description of COVID-SEE: The Scientific Evidence Explorer for COVID-19 Related Research

K Verspoor, S Šuster, Y Otmakhova, S Mendis, Z Zhai, B Fang, JH Lau, T Baldwin, A Jimeno Yepes, D Martinez

Book Chapter  |  2021  |  Lecture Notes in Computer Science

We present COVID-SEE, a system for medical literature discovery based on the concept of information exploration, which builds on s..

Conceptual grounding constraints for truly robust biomedical name representations

P Fivez, S Šuster, W Daelemans

Conference Proceedings  |  2021  |  EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference

Unsupervised concept extraction from clinical text through semantic composition.

Stéphan Tulkens, Simon Šuster, Walter Daelemans

Journal article  |  2019  |  Journal of Biomedical Informatics

Concept extraction is an important step in clinical natural language processing. Once extracted, the use of concepts can improve t..

Patient representation learning and interpretable evaluation using clinical notes.

Madhumita Sushil, Simon Šuster, Kim Luyckx, Walter Daelemans

Journal article  |  2018  |  J Biomed Inform

We have three contributions in this work: 1. We explore the utility of a stacked denoising autoencoder and a paragraph vector mode..

Rule induction for global explanation of trained models

Madhumita Sushil, Simon Šuster, Walter Daelemans

Conference Proceedings  |  2018  |  Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

CliCR: A dataset of clinical case reports for machine reading comprehension

S Suster, W Daelemans

Conference Proceedings  |  2018  |  NAACL HLT 2018 - 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference

We present a new dataset for machine comprehension in the medical domain. Our dataset uses clinical case reports with around 100,0..

Revisiting neural relation classification in clinical notes with external information

Simon Šuster, Madhumita Sushil, Walter Daelemans

Conference Proceedings  |  2018  |  Proceedings of the Ninth International Workshop on Health Text Mining and Information Analysis

Unsupervised context-sensitive spelling correction of english and Dutch clinical free-text with word and character N-Gram embeddings

P Fivez, S Šuster, W Daelemans

Conference Proceedings  |  2017  |  Computational Linguistics in the Netherlands Journal

We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram..

Bilingual learning of multi-sense embeddings with discrete autoencoders

S Šuster, I Titov, G Van Noord

Conference Proceedings  |  2016  |  2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference

We present an approach to learning multi-sense word embeddings relying both on monolingual and bilingual information. Our model co..

Using Distributed Representations to Disambiguate Biomedical and Clinical Concepts

Stephan Tulkens, Simon Suster, Walter Daelemans

Conference Proceedings  |  2016  |  Proceedings of the 15th Workshop on Biomedical Natural Language Processing