Sérgio Nunes


2024

pdf
A Community-Driven Data-to-Text Platform for Football Match Summaries
Pedro Fernandes | Sérgio Nunes | Luís Santos
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Data-to-text systems offer a transformative approach to generating textual content in data-rich environments. This paper describes the architecture and deployment of Prosebot, a community-driven data-to-text platform tailored for generating textual summaries of football matches derived from match statistics. The system enhances the visibility of lower-tier matches, traditionally accessible only through data tables. Prosebot uses a template-based Natural Language Generation (NLG) module to generate initial drafts, which are subsequently refined by the reading community. Comprehensive evaluations, encompassing both human-mediated and automated assessments, were conducted to assess the system’s efficacy. Analysis of the community-edited texts reveals that significant segments of the initial automated drafts are retained, suggesting their high quality and acceptance by the collaborators. Preliminary surveys conducted among platform users highlight a predominantly positive reception within the community.

pdf
Text2Story Lusa: A Dataset for Narrative Analysis in European Portuguese News Articles
Sérgio Nunes | Alípio Mario Jorge | Evelin Amorim | Hugo Sousa | António Leal | Purificação Moura Silvano | Inês Cantante | Ricardo Campos
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Narratives have been the subject of extensive research across various scientific fields such as linguistics and computer science. However, the scarcity of freely available datasets, essential for studying this genre, remains a significant obstacle. Furthermore, datasets annotated with narratives components and their morphosyntactic and semantic information are even scarcer. To address this gap, we developed the Text2Story Lusa datasets, which consist of a collection of news articles in European Portuguese. The first datasets consists of 357 news articles and the second dataset comprises a subset of 117 manually densely annotated articles, totaling over 50 thousand individual annotations. By focusing on texts with substantial narrative elements, we aim to provide a valuable resource for studying narrative structures in European Portuguese news articles. On the one hand, the first dataset provides researchers with data to study narratives from various perspectives. On the other hand, the annotated dataset facilitates research in information extraction and related tasks, particularly in the context of narrative extraction pipelines. Both datasets are made available adhering to FAIR principles, thereby enhancing their utility within the research community.

pdf
Network-based Approach for Stopwords Detection
Felermino D. M. A. Ali | Gabriel de Jesus | Henrique Lopes Cardoso | Sérgio Nunes | Rui Sousa-Silva
Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 2

pdf
Indexing Portuguese NLP Resources with PT-Pump-Up
Rúben Almeida | Ricardo Campos | Alípio Jorge | Sérgio Nunes
Proceedings of the 16th International Conference on Computational Processing of Portuguese - Vol. 2

pdf
Labadain-30k+: A Monolingual Tetun Document-Level Audited Dataset
Gabriel de Jesus | Sérgio Nunes
Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024

This paper introduces Labadain-30k+, a monolingual dataset comprising 33.6k documents in Tetun, a low-resource language spoken in Timor-Leste. The dataset was acquired through web crawling and augmented with Wikipedia documents released by Wikimedia. Both sets of documents underwent thorough manual audits at the document level by native Tetun speakers, resulting in the construction of a Tetun text dataset well-suited for a variety of natural language processing and information retrieval tasks. This dataset was employed to conduct a comprehensive content analysis aimed at providing a nuanced understanding of document composition and the evolution of Tetun documents on the web. The analysis revealed that news articles constitute the predominant documents within the dataset, accounting for 89.87% of the total, followed by Wikipedia documents at 4.34%, and legal and governmental documents at 3.65%, among others. Notably, there was a substantial increase in the number of documents in 2020, indicating 11.75 percentage points rise in document quantity, compared to an average of 4.76 percentage points per year from 2001 to 2023. Moreover, the year 2017, marked by the increased popularity of online news in Tetun, served as a threshold for analyzing the evolution of document writing on the web pre- and post-2017, specifically regarding vocabulary usage. Surprisingly, this analysis showed a significant increase of 6.12 percentage points in the Tetun written adhering to the Tetun official standard. Additionally, the persistence of Portuguese loanwords in that trajectory remained evident, reflecting an increase of 5.09 percentage points.

2019

pdf
Stop PropagHate at SemEval-2019 Tasks 5 and 6: Are abusive language classification results reproducible?
Paula Fortuna | Juan Soler-Company | Sérgio Nunes
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper summarizes the participation of Stop PropagHate team at SemEval 2019. Our approach is based on replicating one of the most relevant works on the literature, using word embeddings and LSTM. After circumventing some of the problems of the original code, we found poor results when applying it to the HatEval contest (F1=0.45). We think this is due mainly to inconsistencies in the data of this contest. Finally, for the OffensEval the classifier performed well (F1=0.74), proving to have a better performance for offense detection than for hate speech.

pdf
A Hierarchically-Labeled Portuguese Hate Speech Dataset
Paula Fortuna | João Rocha da Silva | Juan Soler-Company | Leo Wanner | Sérgio Nunes
Proceedings of the Third Workshop on Abusive Language Online

Over the past years, the amount of online offensive speech has been growing steadily. To successfully cope with it, machine learning are applied. However, ML-based techniques require sufficiently large annotated datasets. In the last years, different datasets were published, mainly for English. In this paper, we present a new dataset for Portuguese, which has not been in focus so far. The dataset is composed of 5,668 tweets. For its annotation, we defined two different schemes used by annotators with different levels of expertise. Firstly, non-experts annotated the tweets with binary labels (‘hate’ vs. ‘no-hate’). Secondly, expert annotators classified the tweets following a fine-grained hierarchical multiple label scheme with 81 hate speech categories in total. The inter-annotator agreement varied from category to category, which reflects the insight that some types of hate speech are more subtle than others and that their detection depends on personal perception. This hierarchical annotation scheme is the main contribution of the presented work, as it facilitates the identification of different types of hate speech and their intersections. To demonstrate the usefulness of our dataset, we carried a baseline classification experiment with pre-trained word embeddings and LSTM on the binary classified data, with a state-of-the-art outcome.

2018

pdf
Merging Datasets for Aggressive Text Identification
Paula Fortuna | José Ferreira | Luiz Pires | Guilherme Routar | Sérgio Nunes
Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018)

This paper presents the approach of the team “groutar” to the shared task on Aggression Identification, considering the test sets in English, both from Facebook and general Social Media. This experiment aims to test the effect of merging new datasets in the performance of classification models. We followed a standard machine learning approach with training, validation, and testing phases, and considered features such as part-of-speech, frequencies of insults, punctuation, sentiment, and capitalization. In terms of algorithms, we experimented with Boosted Logistic Regression, Multi-Layer Perceptron, Parallel Random Forest and eXtreme Gradient Boosting. One question appearing was how to merge datasets using different classification systems (e.g. aggression vs. toxicity). Other issue concerns the possibility to generalize models and apply them to data from different social networks. Regarding these, we merged two datasets, and the results showed that training with similar data is an advantage in the classification of social networks data. However, adding data from different platforms, allowed slightly better results in both Facebook and Social Media, indicating that more generalized models can be an advantage.

2004

pdf
Evaluation of Different Similarity Measures for the Extraction of Multiword Units in a Reinforcement Learning Environment
Gaël Dias | Sérgio Nunes
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)