Search Results for author: Darja Fišer

Found 7 papers, 1 papers with code

Exploring Stylometric and Emotion-Based Features for Multilingual Cross-Domain Hate Speech Detection

no code implementations EACL (WASSA) 2021 Ilia Markov, Nikola Ljubešić, Darja Fišer, Walter Daelemans

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.

Hate Speech Detection

The LiLaH Emotion Lexicon of Croatian, Dutch and Slovene

no code implementations COLING (PEOPLES) 2020 Nikola Ljubešić, Ilia Markov, Darja Fišer, Walter Daelemans

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).

Translation

ParlaMint II: The Show Must Go On

no code implementations ParlaCLARIN (LREC) 2022 Maciej Ogrodniczuk, Petya Osenova, Tomaž Erjavec, Darja Fišer, Nikola Ljubešić, Çağrı Çöltekin, Matyáš Kopp, Meden Katja

In ParlaMint I, a CLARIN-ERIC supported project in pandemic times, a set of comparable and uniformly annotated multilingual corpora for 17 national parliaments were developed and released in 2021.

Parliamentary Discourse Research in Sociology: Literature Review

no code implementations ParlaCLARIN (LREC) 2022 Jure Skubic, Darja Fišer

One of the major sociological research interests has always been the study of political discourse.

Sociology

The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English

no code implementations5 Jun 2019 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 topics, migrants and LGBT, and are manually annotated for different types of socially unacceptable discourse (SUD).

Predicting Concreteness and Imageability of Words Within and Across Languages via Word Embeddings

1 code implementation9 Jul 2018 Nikola Ljubešić, Darja Fišer, Anita Peti-Stantić

We show that the notions of concreteness and imageability are highly predictable both within and across languages, with a moderate loss of up to 20% in correlation when predicting across languages.

Cross-Lingual Transfer Word Embeddings

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