Thomas Demeester

research

∙ 07/30/2023

Distractor generation for multiple-choice questions with predictive prompting and large language models

Large Language Models (LLMs) such as ChatGPT have demonstrated remarkabl...

0 Semere Kiros Bitew, et al. ∙

research

∙ 07/20/2023

Extreme Multi-Label Skill Extraction Training using Large Language Models

Online job ads serve as a valuable source of information for skill requi...

0 Jens-Joris Decorte, et al. ∙

research

∙ 06/02/2023

Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning

Since performing exercises (including, e.g., practice tests) forms a cru...

0 Semere Kiros Bitew, et al. ∙

research

∙ 06/01/2023

Automatic Glossary of Clinical Terminology: a Large-Scale Dictionary of Biomedical Definitions Generated from Ontological Knowledge

Background: More than 400,000 biomedical concepts and some of their rela...

0 François Remy, et al. ∙

research

∙ 05/31/2023

IDAS: Intent Discovery with Abstractive Summarization

Intent discovery is the task of inferring latent intents from a set of u...

0 Maarten De Raedt, et al. ∙

research

∙ 05/22/2023

BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance

Timely and accurate extraction of Adverse Drug Events (ADE) from biomedi...

0 Karel D'Oosterlinck, et al. ∙

research

∙ 05/11/2023

Detecting Idiomatic Multiword Expressions in Clinical Terminology using Definition-Based Representation Learning

This paper shines a light on the potential of definition-based semantic ...

0 François Remy, et al. ∙

research

∙ 02/05/2023

TempEL: Linking Dynamically Evolving and Newly Emerging Entities

In our continuously evolving world, entities change over time and new, p...

0 Klim Zaporojets, et al. ∙

research

∙ 11/15/2022

Neural Bayesian Network Understudy

Bayesian Networks may be appealing for clinical decision-making due to t...

0 Paloma Rabaey, et al. ∙

research

∙ 10/25/2022

Learning to Reuse Distractors to support Multiple Choice Question Generation in Education

Multiple choice questions (MCQs) are widely used in digital learning sys...

0 Semere Kiros Bitew, et al. ∙

research

∙ 10/21/2022

BioLORD: Learning Ontological Representations from Definitions (for Biomedical Concepts and their Textual Descriptions)

This work introduces BioLORD, a new pre-training strategy for producing ...

0 François Remy, et al. ∙

research

∙ 10/21/2022

Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals

For text classification tasks, finetuned language models perform remarka...

0 Maarten De Raedt, et al. ∙

research

∙ 10/12/2022

EduQG: A Multi-format Multiple Choice Dataset for the Educational Domain

We introduce a high-quality dataset that contains 3,397 samples comprisi...

0 Amir Hadifar, et al. ∙

research

∙ 09/13/2022

Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction

Skills play a central role in the job market and many human resources (H...

0 Jens-Joris Decorte, et al. ∙

research

∙ 08/24/2022

Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines

Models for bankruptcy prediction are useful in several real-world scenar...

0 Henri Arno, et al. ∙

research

∙ 06/17/2022

CookDial: A dataset for task-oriented dialogs grounded in procedural documents

This work presents a new dialog dataset, CookDial, that facilitates rese...

0 Yiwei Jiang, et al. ∙

research

∙ 09/20/2021

JobBERT: Understanding Job Titles through Skills

Job titles form a cornerstone of today's human resources (HR) processes....

0 Jens-Joris Decorte, et al. ∙

research

∙ 08/30/2021

Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution

We consider the task of document-level entity linking (EL), where it is ...

0 Klim Zaporojets, et al. ∙

research

∙ 07/05/2021

Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution

We consider a joint information extraction (IE) model, solving named ent...

12 Severine Verlinden, et al. ∙

research

∙ 04/16/2021

A Million Tweets Are Worth a Few Points: Tuning Transformers for Customer Service Tasks

In online domain-specific customer service applications, many companies ...

0 Amir Hadifar, et al. ∙

research

∙ 04/08/2021

A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders

Powerful sentence encoders trained for multiple languages are on the ris...

30 Maarten De Raedt, et al. ∙

research

∙ 09/26/2020

DWIE: an entity-centric dataset for multi-task document-level information extraction

This paper presents DWIE, the 'Deutsche Welle corpus for Information Ext...

0 Klim Zaporojets, et al. ∙

research

∙ 09/11/2020

Solving Math Word Problems by Scoring Equations with Recursive Neural Networks

Solving math word problems is a cornerstone task in assessing language u...

0 Klim Zaporojets, et al. ∙

research

∙ 01/15/2020

Overly Optimistic Prediction Results on Imbalanced Data: Flaws and Benefits of Applying Over-sampling

Information extracted from electrohysterography recordings could potenti...

0 Gilles Vandewiele, et al. ∙

research

∙ 01/14/2020

Block-wise Dynamic Sparseness

Neural networks have achieved state of the art performance across a wide...

5 Amir Hadifar, et al. ∙

research

∙ 11/21/2019

System Identification with Time-Aware Neural Sequence Models

Established recurrent neural networks are well-suited to solve a wide va...

0 Thomas Demeester, et al. ∙

research

∙ 03/13/2019

Sub-event detection from Twitter streams as a sequence labeling problem

This paper introduces improved methods for sub-event detection in social...

0 Giannis Bekoulis, et al. ∙

research

∙ 08/28/2018

Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?

Character-level features are currently used in different neural network-...

0 Fréderic Godin, et al. ∙

research

∙ 08/27/2018

Predefined Sparseness in Recurrent Sequence Models

Inducing sparseness while training neural networks has been shown to yie...

0 Thomas Demeester, et al. ∙

research

∙ 08/21/2018

Adversarial training for multi-context joint entity and relation extraction

Adversarial training (AT) is a regularization method that can be used to...

0 Giannis Bekoulis, et al. ∙

research

∙ 06/25/2018

Prior Attention for Style-aware Sequence-to-Sequence Models

We extend sequence-to-sequence models with the possibility to control th...

0 Lucas Sterckx, et al. ∙

research

∙ 06/20/2018

Jack the Reader - A Machine Reading Framework

Many Machine Reading and Natural Language Understanding tasks require re...

0 Dirk Weissenborn, et al. ∙

research

∙ 05/28/2018

DeepProbLog: Neural Probabilistic Logic Programming

We introduce DeepProbLog, a probabilistic logic programming language tha...

0 Robin Manhaeve, et al. ∙

research

∙ 04/20/2018

Joint entity recognition and relation extraction as a multi-head selection problem

State-of-the-art models for joint entity recognition and relation extrac...

0 Giannis Bekoulis, et al. ∙

research

∙ 01/02/2018

Character-level Recurrent Neural Networks in Practice: Comparing Training and Sampling Schemes

Recurrent neural networks are nowadays successfully used in an abundance...

0 Cedric De Boom, et al. ∙

research

∙ 09/27/2017

An attentive neural architecture for joint segmentation and parsing and its application to real estate ads

In this paper we develop a relatively simple and effective neural joint ...

0 Giannis Bekoulis, et al. ∙

research

∙ 08/22/2017

Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales

The amount of content on online music streaming platforms is immense, an...

0 Cedric De Boom, et al. ∙

research

∙ 08/11/2017

Break it Down for Me: A Study in Automated Lyric Annotation

Comprehending lyrics, as found in songs and poems, can pose a challenge ...

0 Lucas Sterckx, et al. ∙

research

∙ 07/24/2017

Adversarial Sets for Regularising Neural Link Predictors

In adversarial training, a set of models learn together by pursuing comp...

0 Pasquale Minervini, et al. ∙

research

∙ 07/02/2016

Representation learning for very short texts using weighted word embedding aggregation

Short text messages such as tweets are very noisy and sparse in their us...

0 Cedric De Boom, et al. ∙

research

∙ 06/27/2016

Lifted Rule Injection for Relation Embeddings

Methods based on representation learning currently hold the state-of-the...

0 Thomas Demeester, et al. ∙

research

∙ 05/09/2016

Efficiency Evaluation of Character-level RNN Training Schedules

We present four training and prediction schedules from the same characte...

0 Cedric De Boom, et al. ∙

research

∙ 12/02/2015

Learning Semantic Similarity for Very Short Texts

Levering data on social media, such as Twitter and Facebook, requires in...

0 Cedric De Boom, et al. ∙

research

∙ 11/19/2015

Knowledge Base Population using Semantic Label Propagation

A crucial aspect of a knowledge base population system that extracts new...

0 Lucas Sterckx, et al. ∙

Thomas Demeester

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