Search Results for author: Simon Šuster

Found 16 papers, 9 papers with code

Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration

no code implementations EMNLP (NLP-COVID19) 2020 Yulia Otmakhova, Karin Verspoor, Timothy Baldwin, Simon Šuster

Efficient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose.

Topic Models

COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research

no code implementations18 Aug 2020 Karin Verspoor, Simon Šuster, Yulia Otmakhova, Shevon Mendis, Zenan Zhai, Biaoyan Fang, Jey Han Lau, Timothy Baldwin, Antonio Jimeno Yepes, David Martinez

We present COVID-SEE, a system for medical literature discovery based on the concept of information exploration, which builds on several distinct text analysis and natural language processing methods to structure and organise information in publications, and augments search by providing a visual overview supporting exploration of a collection to identify key articles of interest.

Natural Language Processing

Distilling neural networks into skipgram-level decision lists

1 code implementation14 May 2020 Madhumita Sushil, Simon Šuster, Walter Daelemans

For evaluation of explanations, we create a synthetic sepsis-identification dataset, as well as apply our technique on additional clinical and sentiment analysis datasets.

Sentiment Analysis

Why can't memory networks read effectively?

no code implementations16 Oct 2019 Simon Šuster, Madhumita Sushil, Walter Daelemans

Memory networks have been a popular choice among neural architectures for machine reading comprehension and question answering.

Machine Reading Comprehension Question Answering

Rule induction for global explanation of trained models

1 code implementation WS 2018 Madhumita Sushil, Simon Šuster, Walter Daelemans

We find that the output rule-sets can explain the predictions of a neural network trained for 4-class text classification from the 20 newsgroups dataset to a macro-averaged F-score of 0. 80.

Feature Importance Text Classification

Patient representation learning and interpretable evaluation using clinical notes

no code implementations3 Jul 2018 Madhumita Sushil, Simon Šuster, Kim Luyckx, Walter Daelemans

We compare the model performance of the feature set constructed from a bag of words to that obtained from medical concepts.

Denoising General Classification +1

Unsupervised patient representations from clinical notes with interpretable classification decisions

no code implementations14 Nov 2017 Madhumita Sushil, Simon Šuster, Kim Luyckx, Walter Daelemans

To understand and interpret the representations, we explore the best encoded features within the patient representations obtained from the autoencoder model.

Denoising General Classification

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

1 code implementation19 Oct 2017 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.

Spelling Correction

A Short Review of Ethical Challenges in Clinical Natural Language Processing

1 code implementation WS 2017 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.

Natural Language Processing

Using Distributed Representations to Disambiguate Biomedical and Clinical Concepts

2 code implementations WS 2016 Stéphan Tulkens, Simon Šuster, Walter Daelemans

In this paper, we report a knowledge-based method for Word Sense Disambiguation in the domains of biomedical and clinical text.

Word Sense Disambiguation

Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders

1 code implementation NAACL 2016 Simon Šuster, Ivan Titov, Gertjan van Noord

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

Word Embeddings

GLAD: Groningen Lightweight Authorship Detection

1 code implementation6 Sep 2015 Manuela Hürlimann, Benno Weck, Esther van den Berg, Simon Šuster, and Malvina Nissim

We present a simple and effective approach to authorship verification for Dutch, English, Spanish and Greek, which can be easily ported to yet other languages. We train a binary linear classifier both on the features describing known and unknown documents individually, and on the joint features comparing these two types of documents.

Authorship Verification

Word Representations, Tree Models and Syntactic Functions

1 code implementation31 Aug 2015 Simon Šuster, Gertjan van Noord, Ivan Titov

Word representations induced from models with discrete latent variables (e. g.\ HMMs) have been shown to be beneficial in many NLP applications.

named-entity-recognition Named Entity Recognition +1

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

no code implementations7 Feb 2015 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.

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