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Found 51 papers, 13 papers with code
Topic and Emotion Development among Dutch COVID-19 Twitter Communities in the early Pandemic
The paper focuses on a large collection of Dutch tweets from the Netherlands to get an insight into the perception and reactions of users during the early months of the COVID-19 pandemic.
The Corpora They Are a-Changing: a Case Study in Italian Newspapers
The use of automatic methods for the study of lexical semantic change (LSC) has led to the creation of evaluation benchmarks.
How about Time? Probing a Multilingual Language Model for Temporal Relations
This paper presents a comprehensive set of probing experiments using a multilingual language model, XLM-R, for temporal relation classification between events in four languages.
RUG-1-Pegasussers at SemEval-2022 Task 3: Data Generation Methods to Improve Recognizing Appropriate Taxonomic Word Relations
This paper describes our system created for the SemEval 2022 Task 3: Presupposed Taxonomies - Evaluating Neural-network Semantics.
MultiLexNorm: A Shared Task on Multilingual Lexical Normalization
This task is beneficial for downstream analysis, as it provides a way to harmonize (often spontaneous) linguistic variation.
PROTEST-ER: Retraining BERT for Protest Event Extraction
We analyze the effect of further retraining BERT with different domain specific data as an unsupervised domain adaptation strategy for event extraction.
Guiding Principles for Participatory Design-inspired Natural Language Processing
We introduce 9 guiding principles to integrate Participatory Design (PD) methods in the development of Natural Language Processing (NLP) systems.
“Zo Grof !”: A Comprehensive Corpus for Offensive and Abusive Language in Dutch
This paper presents a comprehensive corpus for the study of socially unacceptable language in Dutch.
DALC: the Dutch Abusive Language Corpus
As socially unacceptable language become pervasive in social media platforms, the need for automatic content moderation become more pressing.
A Multilingual Approach to Identify and Classify Exceptional Measures against COVID-19
The COVID-19 pandemic has witnessed the implementations of exceptional measures by governments across the world to counteract its impact.
Wikibio: a Semantic Resource for the Intersectional Analysis of Biographical Events
The corpus, which includes 20 Wikipedia biographies, was compared with five existing corpora to train a model for the biographical event detection task.
Event Causality Identification with Causal News Corpus -- Shared Task 3, CASE 2022
The best F1 scores achieved for Subtask 1 and 2 were 86. 19% and 54. 15%, respectively.
Dead or Murdered? Predicting Responsibility Perception in Femicide News Reports
We then train regression models that predict the salience of GBV participants with respect to different dimensions of perceived responsibility.
The Causal News Corpus: Annotating Causal Relations in Event Sentences from News
Leveraging each of these external datasets for training, we achieved up to approximately 64% F1 on the CNC test set without additional fine-tuning.
SOCIOFILLMORE: A Tool for Discovering Perspectives
SOCIOFILLMORE is a multilingual tool which helps to bring to the fore the focus or the perspective that a text expresses in depicting an event.
Fighting the COVID-19 Infodemic with a Holistic BERT Ensemble
This paper describes the TOKOFOU system, an ensemble model for misinformation detection tasks based on six different transformer-based pre-trained encoders, implemented in the context of the COVID-19 Infodemic Shared Task for English.
GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models
We introduce an approach to multilingual Offensive Language Detection based on the mBERT transformer model.
HateBERT: Retraining BERT for Abusive Language Detection in English
In this paper, we introduce HateBERT, a re-trained BERT model for abusive language detection in English.
Ranked #1 on
Hate Speech Detection
on AbusEval
Lower Bias, Higher Density Abusive Language Datasets: A Recipe
Strategies used until now to increase density of abusive language and obtain more meaningful data overall, include data filtering on the basis of pre-selected keywords and hate-rich sources of data.
I Feel Offended, Don't Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language
However, there is a lack of data sets that take into account the degree of explicitness.
Norm It! Lexical Normalization for Italian and Its Downstream Effects for Dependency Parsing
However, for Italian, there is no benchmark available for lexical normalization, despite the presence of many benchmarks for other tasks involving social media data.
Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic.
BERTje: A Dutch BERT Model
The transformer-based pre-trained language model BERT has helped to improve state-of-the-art performance on many natural language processing (NLP) tasks.
Ranked #3 on
Sentiment Analysis
on DBRD
Italian Event Detection Goes Deep Learning
This paper reports on a set of experiments with different word embeddings to initialize a state-of-the-art Bi-LSTM-CRF network for event detection and classification in Italian, following the EVENTI evaluation exercise.
Crowdsourcing StoryLines: Harnessing the Crowd for Causal Relation Annotation
This paper describes a crowdsourcing experiment on the annotation of plot-like structures in English news articles.
The Circumstantial Event Ontology (CEO)
In this paper we describe the ongoing work on the Circumstantial Event Ontology (CEO), a newly developed ontology for calamity events that models semantic circumstantial relations between event classes.
The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction
This paper reports on the Event StoryLine Corpus (ESC) v1. 0, a new benchmark dataset for the temporal and causal relation detection.
The Content Types Dataset: a New Resource to Explore Semantic and Functional Characteristics of Texts
This paper presents a new resource, called Content Types Dataset, to promote the analysis of texts as a composition of units with specific semantic and functional roles.
Temporal Information Annotation: Crowd vs. Experts
This paper describes two sets of crowdsourcing experiments on temporal information annotation conducted on two languages, i. e., English and Italian.
Crowdsourcing Salient Information from News and Tweets
On the other hand, machines need to understand the information that is published in online data streams and generate concise and meaningful overviews.
NLP and Public Engagement: The Case of the Italian School Reform
In this paper we present PIERINO (PIattaforma per l{'}Estrazione e il Recupero di INformazione Online), a system that was implemented in collaboration with the Italian Ministry of Education, University and Research to analyse the citizens{'} comments given in {\#}labuonascuola survey.
GRaSP: A Multilayered Annotation Scheme for Perspectives
In the last decade, different aspects of linguistic encoding of perspectives have been targeted as separated phenomena through different annotation initiatives.
Enriching the ``Senso Comune'' Platform with Automatically Acquired Data
This paper reports on research activities on automatic methods for the enrichment of the Senso Comune platform.
Customizable SCF Acquisition in Italian
In addition to this, we assigned to the extracted entries of the lexicon a confidence score based on the relative frequency and evaluated the extractor on domain specific data.
Assigning Connotation Values to Events
Sentiment Analysis (SA) and Opinion Mining (OM) have become a popular task in recent years in NLP with the development of language resources, corpora and annotation schemes.
SemEval-2010 Task 13: TempEval-2
Tempeval-2 comprises evaluation tasks for time expressions, events and temporal relations, the latter of which was split up in four sub tasks, motivated by the notion that smaller subtasks would make both data preparation and temporal relation extraction easier.
