66 citations · 231 across the 10 of their papers we have counts for
11 papers · 1 filter
The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings
Michal Mochtak, Peter Rupnik, Nikola Ljubešić
The paper presents a new training dataset of sentences in 7 languages, manually annotated for sentiment, which are used in a series of experiments focused on training a robust sent…
CLASSLA-Stanza: The Next Step for Linguistic Processing of South Slavic Languages
Luka Terčon, Nikola Ljubešić
We present CLASSLA-Stanza, a pipeline for automatic linguistic annotation of the South Slavic languages, which is based on the Stanza natural language processing pipeline. We descr…
Findings of the VarDial Evaluation Campaign 2023
Noëmi Aepli, Çağrı Çöltekin, Rob Van Der Goot +7
This report presents the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2023. The campaign is part of the tenth workshop on Natural Language Proce…
ChatGPT: Beginning of an End of Manual Linguistic Data Annotation? Use Case of Automatic Genre Identification
Taja Kuzman, Igor Mozetič, Nikola Ljubešić
ChatGPT has shown strong capabilities in natural language generation tasks, which naturally leads researchers to explore where its abilities end. In this paper, we examine whether…
The ParlaSent-BCS dataset of sentiment-annotated parliamentary debates from Bosnia-Herzegovina, Croatia, and Serbia
Michal Mochtak, Peter Rupnik, Nikola Ljubešič
Expression of sentiment in parliamentary debates is deemed to be significantly different from that on social media or in product reviews. This paper adds to an emerging body of res…
The GINCO Training Dataset for Web Genre Identification of Documents Out in the Wild
Taja Kuzman, Peter Rupnik, Nikola Ljubešić
This paper presents a new training dataset for automatic genre identification GINCO, which is based on 1,125 crawled Slovenian web documents that consist of 650 thousand words. Eac…
CoSimLex: A Resource for Evaluating Graded Word Similarity in Context
Carlos Santos Armendariz, Matthew Purver, Matej Ulčar +5
State of the art natural language processing tools are built on context-dependent word embeddings, but no direct method for evaluating these representations currently exists. Stand…
The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English
Nikola Ljubešić, Darja Fišer, Tomaž Erjavec
In this paper we present datasets of Facebook comment threads to mainstream media posts in Slovene and English developed inside the Slovene national project FRENK which cover two t…
KAS-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning
Nikola Ljubešić, Darja Fišer, Tomaž Erjavec
This paper presents a dataset and supervised learning experiments for term extraction from Slovene academic texts. Term candidates in the dataset were extracted via morphosyntactic…
Predicting Concreteness and Imageability of Words Within and Across Languages via Word Embeddings
Nikola Ljubešić, Darja Fišer, Anita Peti-Stantić
The notions of concreteness and imageability, traditionally important in psycholinguistics, are gaining significance in semantic-oriented natural language processing tasks. In this…
Bleaching Text: Abstract Features for Cross-lingual Gender Prediction
Rob van der Goot, Nikola Ljubešić, Ian Matroos +2
Gender prediction has typically focused on lexical and social network features, yielding good performance, but making systems highly language-, topic-, and platform-dependent. Cros…