200 citations · 515 across the 16 of their papers we have counts for
19 papers
Improved Allergy Wheal Detection for the Skin Prick Automated Test Device
Rembert Daems, Sven Seys, Valérie Hox +21
Background: The skin prick test (SPT) is the gold standard for diagnosing sensitization to inhalant allergies. The Skin Prick Automated Test (SPAT) device was designed for increase…
Neural Probabilistic Logic Programming in DeepProbLog
Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig +2
We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learni…
Sub-event detection from Twitter streams as a sequence labeling problem
Giannis Bekoulis, Johannes Deleu, Thomas Demeester +1
This paper introduces improved methods for sub-event detection in social media streams, by applying neural sequence models not only on the level of individual posts, but also direc…
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?
Fréderic Godin, Kris Demuynck, Joni Dambre +2
Character-level features are currently used in different neural network-based natural language processing algorithms. However, little is known about the character-level patterns th…
Predefined Sparseness in Recurrent Sequence Models
Thomas Demeester, Johannes Deleu, Fréderic Godin +1
Inducing sparseness while training neural networks has been shown to yield models with a lower memory footprint but similar effectiveness to dense models. However, sparseness is ty…
Adversarial training for multi-context joint entity and relation extraction
Giannis Bekoulis, Johannes Deleu, Thomas Demeester +1
Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We sho…
Prior Attention for Style-aware Sequence-to-Sequence Models
Lucas Sterckx, Johannes Deleu, Chris Develder +1
We extend sequence-to-sequence models with the possibility to control the characteristics or style of the generated output, via attention that is generated a priori (before decodin…
DeepProbLog: Neural Probabilistic Logic Programming
Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig +2
We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning tech…
An attentive neural architecture for joint segmentation and parsing and its application to real estate ads
Giannis Bekoulis, Johannes Deleu, Thomas Demeester +1
In processing human produced text using natural language processing (NLP) techniques, two fundamental subtasks that arise are (i) segmentation of the plain text into meaningful sub…
Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales
Cedric De Boom, Rohan Agrawal, Samantha Hansen +5
The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice…
Break it Down for Me: A Study in Automated Lyric Annotation
Lucas Sterckx, Jason Naradowsky, Bill Byrne +2
Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike. This motivates the need for systems that can understand the ambiguity an…
Adversarial Sets for Regularising Neural Link Predictors
Pasquale Minervini, Thomas Demeester, Tim Rocktäschel +1
In adversarial training, a set of models learn together by pursuing competing goals, usually defined on single data instances. However, in relational learning and other non-i.i.d d…
Resource Selection for Federated Search on the Web
Dong Nguyen, Thomas Demeester, Dolf Trieschnigg +1
A publicly available dataset for federated search reflecting a real web environment has long been absent, making it difficult for researchers to test the validity of their federate…
Representation learning for very short texts using weighted word embedding aggregation
Cedric De Boom, Steven Van Canneyt, Thomas Demeester +1
Short text messages such as tweets are very noisy and sparse in their use of vocabulary. Traditional textual representations, such as tf-idf, have difficulty grasping the semantic…
Lifted Rule Injection for Relation Embeddings
Thomas Demeester, Tim Rocktäschel, Sebastian Riedel
Methods based on representation learning currently hold the state-of-the-art in many natural language processing and knowledge base inference tasks. Yet, a major challenge is how t…
Efficiency Evaluation of Character-level RNN Training Schedules
Cedric De Boom, Sam Leroux, Steven Bohez +3
We present four training and prediction schedules from the same character-level recurrent neural network. The efficiency of these schedules is tested in terms of model effectivenes…
Learning Semantic Similarity for Very Short Texts
Cedric De Boom, Steven Van Canneyt, Steven Bohez +2
Levering data on social media, such as Twitter and Facebook, requires information retrieval algorithms to become able to relate very short text fragments to each other. Traditional…
Predicting Relevance based on Assessor Disagreement: Analysis and Practical Applications for Search Evaluation
Thomas Demeester, Robin Aly, Djoerd Hiemstra +2
Evaluation of search engines relies on assessments of search results for selected test queries, from which we would ideally like to draw conclusions in terms of relevance of the re…
Knowledge Base Population using Semantic Label Propagation
Lucas Sterckx, Thomas Demeester, Johannes Deleu +1
A crucial aspect of a knowledge base population system that extracts new facts from text corpora, is the generation of training data for its relation extractors. In this paper, we…