activity
20152017
most citedUnsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings

13 citations · 14 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CL20171 cited

Unsupervised patient representations from clinical notes with interpretable classification decisions

Madhumita Sushil, Simon Šuster, Kim Luyckx +1

We have two main contributions in this work: 1. We explore the usage of a stacked denoising autoencoder, and a paragraph vector model to learn task-independent dense patient repres…

cs.CL201713 cited

Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings

Pieter Fivez, Simon Šuster, Walter Daelemans

We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings. Our method generates misspelling repl…

cs.CL2017

A Short Review of Ethical Challenges in Clinical Natural Language Processing

Simon Šuster, Stéphan Tulkens, Walter Daelemans

Clinical NLP has an immense potential in contributing to how clinical practice will be revolutionized by the advent of large scale processing of clinical records. However, this pot…

cs.CL2016

Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders

Simon Šuster, Ivan Titov, Gertjan van Noord

We present an approach to learning multi-sense word embeddings relying both on monolingual and bilingual information. Our model consists of an encoder, which uses monolingual and b…

cs.CL2015

An investigation into language complexity of World-of-Warcraft game-external texts

Simon Šuster

We present a language complexity analysis of World of Warcraft (WoW) community texts, which we compare to texts from a general corpus of web English. Results from several complexit…