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20192025
most citedHelping results assessment by adding explainable elements to the deep relevance matching model

24 citations · 47 across the 7 of their papers we have counts for

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7 papers · 1 filter

cs.CL2025

Dataset Creation for Visual Entailment using Generative AI

Rob Reijtenbach, Suzan Verberne, Gijs Wijnholds

In this paper we present and validate a new synthetic dataset for training visual entailment models. Existing datasets for visual entailment are small and sparse compared to datase…

cs.CL20241 cited

Undesirable Memorization in Large Language Models: A Survey

Ali Satvaty, Suzan Verberne, Fatih Turkmen

While recent research increasingly showcases the remarkable capabilities of Large Language Models (LLMs), it is equally crucial to examine their associated risks. Among these, priv…

cs.CL2024

Biomedical Entity Linking for Dutch: Fine-tuning a Self-alignment BERT Model on an Automatically Generated Wikipedia Corpus

Fons Hartendorp, Tom Seinen, Erik van Mulligen +1

Biomedical entity linking, a main component in automatic information extraction from health-related texts, plays a pivotal role in connecting textual entities (such as diseases, dr…

cs.CL2023

ChiSCor: A Corpus of Freely Told Fantasy Stories by Dutch Children for Computational Linguistics and Cognitive Science

Bram M. A. van Dijk, Max J. van Duijn, Suzan Verberne +1

In this resource paper we release ChiSCor, a new corpus containing 619 fantasy stories, told freely by 442 Dutch children aged 4-12. ChiSCor was compiled for studying how children…

cs.CL20231 cited

Political corpus creation through automatic speech recognition on EU debates

Hugo de Vos, Suzan Verberne

In this paper, we present a transcribed corpus of the LIBE committee of the EU parliament, totalling 3.6 Million running words. The meetings of parliamentary committees of the EU a…

cs.CL2021

Small data problems in political research: a critical replication study

Hugo de Vos, Suzan Verberne

In an often-cited 2019 paper on the use of machine learning in political research, Anastasopoulos & Whitford (A&W) propose a text classification method for tweets related to organi…