Search Results for author:
Found 72 papers, 22 papers with code
The LiLaH Emotion Lexicon of Croatian, Dutch and Slovene
We further showcase the usage of the lexicons by calculating the difference in emotion distributions in texts containing and not containing socially unacceptable discourse, comparing them across four languages (English, Croatian, Dutch, Slovene) and two topics (migrants and LGBT).
Exploring Stylometric and Emotion-Based Features for Multilingual Cross-Domain Hate Speech Detection
In this paper, we describe experiments designed to evaluate the impact of stylometric and emotion-based features on hate speech detection: the task of classifying textual content into hate or non-hate speech classes.
Contextual explanation rules for neural clinical classifiers
For evaluation of explanations, we create a synthetic sepsis-identification dataset, as well as apply our technique on additional clinical and sentiment analysis datasets.
Are we there yet? Exploring clinical domain knowledge of BERT models
We explore whether state-of-the-art BERT models encode sufficient domain knowledge to correctly perform domain-specific inference.
Scalable Few-Shot Learning of Robust Biomedical Name Representations
Recent research on robust representations of biomedical names has focused on modeling large amounts of fine-grained conceptual distinctions using complex neural encoders.
Integrating Higher-Level Semantics into Robust Biomedical Name Representations
It has not yet been empirically confirmed that training biomedical name encoders on fine-grained distinctions automatically leads to bottom-up encoding of such higher-level semantics.
Improving Cross-Domain Hate Speech Detection by Reducing the False Positive Rate
Hate speech detection is an actively growing field of research with a variety of recently proposed approaches that allowed to push the state-of-the-art results.
Improving Hate Speech Type and Target Detection with Hateful Metaphor Features
We study the usefulness of hateful metaphorsas features for the identification of the type and target of hate speech in Dutch Facebook comments.
Neural Machine Translation of Artwork Titles Using Iconclass Codes
We investigate the use of Iconclass in the context of neural machine translation for NL<->EN artwork titles.
Domain- and Task-Adaptation for VaccinChatNL, a Dutch COVID-19 FAQ Answering Corpus and Classification Model
In addition, we show that large groups of semantically similar questions are important for obtaining well-performing intent classification models.
Mapping probability word problems to executable representations
While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically.
The Role of Context in Detecting the Target of Hate Speech
Online hate speech detection is an inherently challenging task that has recently received much attention from the natural language processing community.
Bilingual BSARD: Extending Statutory Article Retrieval to Dutch
Using bBSARD, we conduct extensive benchmarking of retrieval models available for Dutch and French.
Bag of Lies: Robustness in Continuous Pre-training BERT
Using continuous pre-training, we control what entity knowledge is available to the model.
PersonalityChat: Conversation Distillation for Personalized Dialog Modeling with Facts and Traits
The new wave of Large Language Models (LLM) has offered an efficient tool to curate sizeable conversational datasets.
Open-Domain Dialog Evaluation using Follow-Ups Likelihood
Automatic evaluation of open-domain dialogs remains an unsolved problem.
CoNTACT: A Dutch COVID-19 Adapted BERT for Vaccine Hesitancy and Argumentation Detection
We present CoNTACT: a Dutch language model adapted to the domain of COVID-19 tweets.
Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations
The perturbed data, models, and code are available for reproduction at https://github. com/cmry/augtox
Teach Me What to Say and I Will Learn What to Pick: Unsupervised Knowledge Selection Through Response Generation with Pretrained Generative Models
In this work we study the unsupervised selection abilities of pre-trained generative models (e. g. BART) and show that by adding a score-and-aggregate module between encoder and decoder, they are capable of learning to pick the proper knowledge through minimising the language modelling loss (i. e. without having access to knowledge labels).
MFAQ: a Multilingual FAQ Dataset
In this paper, we present the first multilingual FAQ dataset publicly available.
ConveRT for FAQ Answering
While powerful and efficient retrieval-based models exist for English, it is rarely the case for other languages for which the same amount of training data is not available.
Conceptual Grounding Constraints for Truly Robust Biomedical Name Representations
Effective representation of biomedical names for downstream NLP tasks requires the encoding of both lexical as well as domain-specific semantic information.
A Deep Generative Approach to Native Language Identification
Native language identification (NLI) {--} identifying the native language (L1) of a person based on his/her writing in the second language (L2) {--} is useful for a variety of purposes, including marketing, security, and educational applications.
Sarcasm Detection Using an Ensemble Approach
We present an ensemble approach for the detection of sarcasm in Reddit and Twitter responses in the context of The Second Workshop on Figurative Language Processing held in conjunction with ACL 2020.
Character-level Transformer-based Neural Machine Translation
To stimulate further research in this area and close the gap with subword-level NMT, we make all our code and models publicly available.
Distilling neural networks into skipgram-level decision lists
For evaluation of explanations, we create a synthetic sepsis-identification dataset, as well as apply our technique on additional clinical and sentiment analysis datasets.
Streaming Language-Specific Twitter Data with Optimal Keywords
In these cases, key phrases that limit finding the competitive language are selected, and overall recall on the target language also decreases.
Orthographic Codes and the Neighborhood Effect: Lessons from Information Theory
We consider the orthographic neighborhood effect: the effect that words with more orthographic similarity to other words are read faster.
The European Language Technology Landscape in 2020: Language-Centric and Human-Centric AI for Cross-Cultural Communication in Multilingual Europe
Multilingualism is a cultural cornerstone of Europe and firmly anchored in the European treaties including full language equality.
Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity
The detection of online cyberbullying has seen an increase in societal importance, popularity in research, and available open data.
Why can't memory networks read effectively?
Memory networks have been a popular choice among neural architectures for machine reading comprehension and question answering.
A weakly supervised sequence tagging and grammar induction approach to semantic frame slot filling
This paper describes continuing work on semantic frame slot filling for a command and control task using a weakly-supervised approach.
Effective weakly supervised semantic frame induction using expression sharing in hierarchical hidden Markov models
We present a framework for the induction of semantic frames from utterances in the context of an adaptive command-and-control interface.
From Strings to Other Things: Linking the Neighborhood and Transposition Effects in Word Reading
We conclude that the neighborhood effect is unlikely to have a perceptual basis, but is more likely to be the result of items co-activating after recognition.
Predicting Adolescents' Educational Track from Chat Messages on Dutch Social Media
We aim to predict Flemish adolescents{'} educational track based on their Dutch social media writing.
Revisiting neural relation classification in clinical notes with external information
Recently, segment convolutional neural networks have been proposed for end-to-end relation extraction in the clinical domain, achieving results comparable to or outperforming the approaches with heavy manual feature engineering.
Multilingual Cross-domain Perspectives on Online Hate Speech
In this report, we present a study of eight corpora of online hate speech, by demonstrating the NLP techniques that we used to collect and analyze the jihadist, extremist, racist, and sexist content.
Rule induction for global explanation of trained models
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.
Enhancing General Sentiment Lexicons for Domain-Specific Use
Lexicon based methods for sentiment analysis rely on high quality polarity lexicons.
Exploring Classifier Combinations for Language Variety Identification
This paper describes CLiPS{'}s submissions for the Discriminating between Dutch and Flemish in Subtitles (DFS) shared task at VarDial 2018.
Patient representation learning and interpretable evaluation using clinical notes
We compare the model performance of the feature set constructed from a bag of words to that obtained from medical concepts.
CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension
We present a new dataset for machine comprehension in the medical domain.
Ranked #1 on
Question Answering
on CliCR
Automatic Detection of Cyberbullying in Social Media Text
While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online.
Unsupervised patient representations from clinical notes with interpretable classification decisions
To understand and interpret the representations, we explore the best encoded features within the patient representations obtained from the autoencoder model.
Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings
We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings.
Simple Queries as Distant Labels for Predicting Gender on Twitter
The majority of research on extracting missing user attributes from social media profiles use costly hand-annotated labels for supervised learning.
Towards the Improvement of Automatic Emotion Pre-annotation with Polarity and Subjective Information
Our objective in this paper is to show the pre-annotation process, as well as to evaluate the usability of subjective and polarity information in this process.
Assessing the Stylistic Properties of Neurally Generated Text in Authorship Attribution
Recent applications of neural language models have led to an increased interest in the automatic generation of natural language.
Unsupervised Context-Sensitive Spelling Correction of Clinical Free-Text with Word and Character N-Gram Embeddings
We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings.
A Short Review of Ethical Challenges in Clinical Natural Language Processing
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.
A Dictionary-based Approach to Racism Detection in Dutch Social Media
The best-performing model used the manually cleaned dictionary and obtained an F-score of 0. 46 for the racist class on a test set consisting of unseen Dutch comments, retrieved from the same sites used for the training set.
Using Distributed Representations to Disambiguate Biomedical and Clinical Concepts
In this paper, we report a knowledge-based method for Word Sense Disambiguation in the domains of biomedical and clinical text.
Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource
With this research, we provide the embeddings themselves, the relation evaluation task benchmark for use in further research, and demonstrate how the benchmarked embeddings prove a useful unsupervised linguistic resource, effectively used in a downstream task.
TwiSty: A Multilingual Twitter Stylometry Corpus for Gender and Personality Profiling
Personality profiling is the task of detecting personality traits of authors based on writing style.
Predicting the Effectiveness of Self-Training: Application to Sentiment Classification
The goal of this paper is to investigate the connection between the performance gain that can be obtained by selftraining and the similarity between the corpora used in this approach.
The Effects of Age, Gender and Region on Non-standard Linguistic Variation in Online Social Networks
We present a corpus-based analysis of the effects of age, gender and region of origin on the production of both "netspeak" or "chatspeak" features and regional speech features in Flemish Dutch posts that were collected from a Belgian online social network platform.
CLiPS Stylometry Investigation (CSI) corpus: A Dutch corpus for the detection of age, gender, personality, sentiment and deception in text
We performed a supervised machine learning experiment using the SVM algorithm in a 10-fold cross-validation setup.
The Strategic Impact of META-NET on the Regional, National and International Level
This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of funding for LT topics.
The Netlog Corpus. A Resource for the Study of Flemish Dutch Internet Language
Although in recent years numerous forms of Internet communication ― such as e-mail, blogs, chat rooms and social network environments ― have emerged, balanced corpora of Internet speech with trustworthy meta-information (e. g. age and gender) or linguistic annotations are still limited.
ConanDoyle-neg: Annotation of negation cues and their scope in Conan Doyle stories
In this paper we present ConanDoyle-neg, a corpus of stories by Conan Doyle annotated with negation information.
``Vreselijk mooi!'' (terribly beautiful): A Subjectivity Lexicon for Dutch Adjectives.
The lexicon is a dictionary of 1, 100 adjectives that occur frequently in online product reviews, manually annotated with polarity strength, subjectivity and intensity, for each word sense.
Forgetting Exceptions is Harmful in Language Learning
We provide explanations for both results in terms of the properties of the natural language processing tasks and the learning algorithms.
MBT: A Memory-Based Part of Speech Tagger-Generator
In this paper we show that a large-scale application of the memory-based approach is feasible: we obtain a tagging accuracy that is on a par with that of known statistical approaches, and with attractive space and time complexity properties when using {\em IGTree}, a tree-based formalism for indexing and searching huge case bases.}
